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ml application engineer ai inference model optimization staff senior staff level riyadh ksa

Qualcomm

<div>
 <strong>
  Company
  <br/>
  <br/>
 </strong>
 Qualcomm Middle East Information Technology Company LLC
 <br/>
 <br/>
 <strong>
  Job Area
  <br/>
  <br/>
 </strong>
 Engineering Group, Engineering Group &gt; Software Engineering
 <br/>
 <br/>
 <strong>
  General Summary
  <br/>
  <br/>
 </strong>
 <strong>
  About Us
  <br/>
  <br/>
 </strong>
 Qualcomm is enabling a world where everyone and everything can be intelligently connected. You interact with products and technologies made possible by Qualcomm every day, including intelligent edge devices, next-generation computing platforms, and advanced AI solutions. Qualcomm&rsquo;s leadership in AI, high‑performance compute, and connectivity is driving innovation across cloud, edge, and data center environments - delivering scalable, power‑efficient platforms that power the next generation of intelligent infrastructure.
 <br/>
 <br/>
 <strong>
  About The Role
  <br/>
  <br/>
 </strong>
 Qualcomm is seeking
 <strong>
  Machine Learning Applications Engineer &ndash; AI Inference &amp; Model Optimization
 </strong>
 to support the enablement of rack-scale deep learning workloads on advanced Qualcomm AI inference accelerators. These accelerators utilize Qualcomm's expertise in hardware-accelerated AI to deliver high-performance, energy-efficient generative AI and computer vision inference solutions for modern data centers.
 <br/>
 <br/>
 This is a
 <strong>
  customer
 </strong>
 ‑
 <strong>
  facing, highly technical role
 </strong>
 focused on
 <strong>
  porting, optimizing, and validating deep learning AI models
 </strong>
 on production systems, and enabling Qualcomm&rsquo;s partners to develop and deploy advanced machine learning applications - including computer vision, speech, generative AI and state of the art multimodal reasoning models - using popular frameworks such as PyTorch, TensorFlow, and ONNX on Qualcomm Cloud AI accelerators. Key responsibilities include evaluating models for throughput, latency, and accuracy; profiling and optimizing model performance; building robust application pipelines; integrating customer frameworks; and contributing to documentation, training, and demonstrations.
 <br/>
 <br/>
 The role requires strong expertise in AI models, quantization, performance optimization, and deployment, plus the ability to shape architecture, workload sizing, and system design. It also requires experience with deep learning model development across hardware platforms, solid programming skills, collaboration with cross-functional teams, and proficiency in machine learning frameworks, Linux, and container orchestration tools.
 <br/>
 <br/>
 The ideal candidate can effectively
 <strong>
  bridge AI model requirements &harr; hardware capabilities &harr; customer expectations
 </strong>
 , guiding customers from
 <strong>
  model selection &rarr; hardware sizing &rarr; deployment decisions &rarr; production readiness.
  <br/>
  <br/>
 </strong>
 <strong>
  What You&rsquo;ll Do
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   AI Model Porting &amp; Optimization
  </li>
  <li>
   Deploy, optimize and scale deep learning AI models onto accelerator‑based data center platforms, including:
  </li>
  <li>
   Model conversion workflows
  </li>
  <li>
   Quantization techniques (INT8 / mixed precision)
  </li>
  <li>
   Runtime integration and optimization
  </li>
  <li>
   Integrate ML models onto Qualcomm&rsquo;s Cloud AI ML stack from frameworks such as PyTorch, TensorFlow, and ONNX.
  </li>
  <li>
   Drive improvements in model throughput, latency, and accuracy, with clear trade‑off analysis.
  </li>
  <li>
   Build, test, and deploy scalable inference pipelines using serving frameworks such as vLLM, TGI, and Triton.
  </li>
  <li>
   Optimize workloads for LLM and GenAI models across both multi-SoC and multi-card architectures.
  </li>
  <li>
   Collaborate with engineering teams to analyze and refine training and inference for advanced deep learning applications.
  </li>
  <li>
   Identify bottlenecks across compute, memory, and runtime, and guide optimization strategies.
  </li>
  <li>
   Contribute to Qualcomm&rsquo;s Cloud AI GitHub repository and developer documentation, sharing technical best practices and solutions.
  </li>
  <li>
   Develop and integrate end-to-end ML application pipelines with customer frameworks and libraries.
  </li>
  <li>
   Customer‑Facing Technical Engagement
  </li>
  <li>
   Act as a trusted technical advisor for customers deploying AI workloads.
  </li>
  <li>
   Engage in hardware sizing and architecture discussions, aligning model requirements with infrastructure capabilities.
  </li>
  <li>
   Provide technical guidance on:
  </li>
  <li>
   AI model selection
  </li>
  <li>
   Deployment feasibility
  </li>
  <li>
   System architecture and performance expectations
  </li>
  <li>
   Lead discussions on model capabilities and limitations based on real customer use cases.
  </li>
  <li>
   Model&ndash;Infrastructure Alignment
  </li>
  <li>
   Assess and evaluate AI model requirements and recommend alternative model approaches when necessary.
  </li>
  <li>
   Align model characteristics (latency, throughput, accuracy) with accelerator and system capabilities.
  </li>
  <li>
   Connect model requirements with:
  </li>
  <li>
   Memory constraints
  </li>
  <li>
   Accelerator architecture
  </li>
  <li>
   Scaling limitations
  </li>
  <li>
   Support customers in defining model selection strategies based on deployment realities.
  </li>
  <li>
   Performance &amp; Scalability Engineering
  </li>
  <li>
   Evaluate performance characteristics of AI models in production scenarios, including:
  </li>
  <li>
   Throughput expectations
  </li>
  <li>
   Latency targets
  </li>
  <li>
   Concurrency behavior
  </li>
  <li>
   Guide architecture decisions around:
  </li>
  <li>
   Scaling strategies (horizontal vs vertical)
  </li>
  <li>
   Hardware deployment sizing
  </li>
  <li>
   Contribute to discussions on:
  </li>
  <li>
   Workload scalability limits
  </li>
  <li>
   Impact of model selection on system performance and efficiency
  </li>
  <li>
   Provide insights into capacity planning and infrastructure optimization.
  </li>
  <li>
   End‑to‑End AI Pipeline Design
  </li>
  <li>
   Drive discussions around end‑to‑end AI pipelines, including:
  </li>
  <li>
   Multi‑model workflows (e.g., detection + tracking + recognition)
  </li>
  <li>
   Data preprocessing and post‑processing stages
  </li>
  <li>
   Guide decisions on video and data processing stacks, including:
  </li>
  <li>
   Video pipeline choices (e.g., FFMPEG vs GStreamer)
  </li>
  <li>
   Integration into inference pipelines
  </li>
  <li>
   Ensure pipelines are aligned with:
  </li>
  <li>
   Performance requirements
  </li>
  <li>
   Hardware capabilities
  </li>
  <li>
   Real‑time constraints
  </li>
  <li>
   Model Trade‑off Analysis &amp; Validation
  </li>
  <li>
   Highlight and explain trade‑offs between:
  </li>
  <li>
   Accuracy vs compatibility
  </li>
  <li>
   Model quality vs deployment feasibility
  </li>
  <li>
   Support decision‑making on:
  </li>
  <li>
   Model simplification vs performance gains
  </li>
  <li>
   Precision vs efficiency trade‑offs
  </li>
  <li>
   Lead or support model capability validation in deployment environments.
  </li>
  <li>
   Collaborate with customers to define:
  </li>
  <li>
   Inference assumptions
  </li>
  <li>
   Model sizing strategies for large‑scale workloads
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Required Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Bachelor&rsquo;s degree in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
  </li>
  <li>
   10&ndash;15+ years of experience in:
  </li>
  <li>
   Deep learning model development or deployment experience on CPUs/GPUs/ASICs.
  </li>
  <li>
   Inference systems and optimization
  </li>
  <li>
   Data center or edge AI platforms
  </li>
  <li>
   Strong experience with:
  </li>
  <li>
   Model quantization and optimization techniques
  </li>
  <li>
   AI model frameworks (e.g., PyTorch, TensorFlow)
  </li>
  <li>
   Model deployment pipelines
  </li>
  <li>
   Excellent C/C++/Python programming and software design skills, including debugging, and performance analysis.
  </li>
  <li>
   Hands on expertise with Linux-based systems, low level software, drivers, and system bring up.
  </li>
  <li>
   Proven ability to analyze and optimize model performance in production environments.
  </li>
  <li>
   Solid understanding of:
  </li>
  <li>
   AI inference hardware constraints
  </li>
  <li>
   System level performance bottlenecks
  </li>
  <li>
   Strong communication skills and experience in customer facing technical roles.
  </li>
  <li>
   Willingness to travel for customer engagements and strategic reviews.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Skilled in deploying models on platforms that use hardware accelerators for inference.
  </li>
  <li>
   Experienced with managing multi-model workflows and building real-time AI systems, including computer vision, video, and analytics projects.
  </li>
  <li>
   Knowledgeable about distributed inference methods and handling large-scale model deployments.
  </li>
  <li>
   Proficient in developing and maintaining video processing workflows and using relevant software frameworks.
  </li>
  <li>
   Deep understanding of how system-level decisions affect performance in actual deployment environments.
  </li>
  <li>
   Capable of simplifying complex technical ideas into straightforward, useful advice for clients.
  </li>
  <li>
   Hands-on experience running deep learning models on popular ML frameworks such as PyTorch, TensorFlow, ONNX
  </li>
  <li>
   Experience developing software solutions that run in Linux environments with containers and orchestration
  </li>
  <li>
   Experience with Source code and configuration management tools, Git knowledge is required.
  </li>
  <li>
   Customer-facing experience translating customer requirements into technical solutions (discovery, scoping, success criteria, and execution plans).
  </li>
  <li>
   Proven ability to build and deliver technical demos, proofs-of-concept, and reference applications for ML/GenAI workloads.
  </li>
  <li>
   Strong technical writing skills to produce customer-ready documentation (getting started guides, deployment runbooks, troubleshooting guides) and deliver partner training sessions.
  </li>
  <li>
   Experience driving issue triage and technical escalations with customers, coordinating across product, hardware, and software engineering teams to resolution.
  </li>
  <li>
   Excellent stakeholder management and communication skills: present complex technical concepts clearly to both engineering and non-engineering audiences.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Why Join Qualcomm
  <br/>
  <br/>
 </strong>
 At Qualcomm, you&rsquo;ll work at the intersection of
 <strong>
  AI silicon, system architecture, and real
 </strong>
 ‑
 <strong>
  world deployment
 </strong>
 . You will engage directly with strategic customers, influence next‑generation AI data center platforms, and help define scalable, power‑efficient infrastructure for the AI era. This role provides a unique opportunity to shape both
 <strong>
  technology direction and customer outcomes
 </strong>
 , while working with world‑class engineering and product teams.
 <br/>
 <br/>
 <strong>
  What's On Offer
  <br/>
  <br/>
 </strong>
 Apart from working with great people, we offer the below:
 <br/>
 <br/>
 <ul>
  <li>
   Salary including housing &amp; transport allowance
  </li>
  <li>
   Stock (RSU's) and performance related bonus
  </li>
  <li>
   16 weeks fully paid Maternity Leave
  </li>
  <li>
   6 weeks fully paid Paternity Leave
  </li>
  <li>
   Employee stock purchase scheme
  </li>
  <li>
   Child Education Allowance
  </li>
  <li>
   Relocation and immigration support (if needed)
  </li>
  <li>
   Life and Medical Insurance
  </li>
  <li>
   Live+ Well Reimbursement for health and recreational membership fees
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Minimum Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 6+ years of Software Engineering or related work experience.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 OR
 <br/>
 <br/>
 Master's degree in Engineering, Information Systems, Computer Science, or related field and 5+ years of Software Engineering or related work experience.
 <br/>
 <br/>
 OR
 <br/>
 <br/>
 PhD in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience.
 <br/>
 <br/>
 <ul>
  <li>
   3+ years of work experience with Programming Language such as C, C++, Java, Python, etc.
  </li>
  <li>
   References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).
 <br/>
 <br/>
 Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
 <br/>
 <br/>
 <strong>
  To all Staffing and Recruiting Agencies
 </strong>
 : Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.
 <br/>
 <br/>
 If you would like more information about this role, please contact Qualcomm Careers.
</div>


ML Application Engineer – AI Inference & Model Optimization (Staff/Senior Staff level) - Riyadh, KSA

<div><h2 class="jobSectionHeader"><b>Company:
</b></h2>Qualcomm Middle East Information Technology Company LLC
<p></p><h2 class="jobSectionHeader"><b>Job Area:
</b></h2>Engineering Group, Engineering Group &gt; Software Engineering
<p></p><p><b>General Summary:
</b></p><p></p><div><div><p><b>About Us
</b></p></div><div><p>Qualcomm is enabling a world where everyone and everything can be intelligently connected. You interact with products and technologies made possible by Qualcomm every day, including intelligent edge devices, next-generation computing platforms, and advanced AI solutions. Qualcomm&rsquo;s leadership in AI, high ‑ performance compute, and connectivity is driving innovation across cloud, edge, and data center environments - delivering scalable, power ‑ efficient platforms that power the next generation of intelligent infrastructure.<br>
</p></div><p></p><div><p><b>About the Role
</b></p></div><div><p>Qualcomm is seeking <b>Machine Learning Applications Engineer </b><b>&ndash; AI </b><b>Inference &amp; Model Optimization </b>to support the enablement of rack-scale deep learning workloads on advanced Qualcomm AI inference accelerator s . These accelerators utilize Qualcomm's expertise in hardware-accelerated AI to deliver high-performance, energy-efficient generative AI and computer vision inference solutions for modern data centers.
</p></div><p></p><div><p>This is a <b>customer </b>‑ <b>facing, highly technical role </b>focused on <b>porting, optimizing, and validating </b><b>deep learning </b><b>AI models </b>on production systems, and enabling Qualcomm&rsquo;s partners to develop and deploy advanced machine learning applications - including computer vision, speech, generative AI and state of the art multimodal reasoning models - using popular frameworks such as PyTorch , TensorFlow, and ONNX on Qualcomm Cloud AI accelerators. Key responsibilities include evaluating models for throughput, latency, and accuracy; profiling and optimizing model performance; building robust application pipelines; integrating customer frameworks; and contributing to documentation, training, and demonstrations.
</p></div><p></p><div><p>The role requires strong expertise in AI models, quantization, performance optimization, and deployment, plus the ability to shape architecture, workload sizing, and system design. It also requires experience with deep learning model development across hardware platforms, solid programming skills, collaboration with cross-functional teams, and proficiency in machine learning frameworks, Linux, and container orchestration tools.
</p></div><p></p><div><p>The ideal candidate can effectively <b>bridge AI model requirements hardware capabilities customer expectations </b>, guiding customers from <b>model selection hardware sizing deployment decisions production readiness.
</b></p></div><p></p><div><p><b>What You&rsquo;ll Do
</b></p></div><div><p><b>A) AI Model Porting &amp; Optimization
</b></p></div><div><ul><li><p>Deploy, optimize and scale deep learning AI models onto accelerator ‑ based data center platforms, including:
</p></li></ul></div><div><ul><li><p>Model conversion workflows
</p></li></ul></div><div><ul><li><p>Quantization techniques (INT8 / mixed precision)
</p></li></ul></div><div><ul><li><p>Runtime integration and optimization
</p></li></ul></div><div><ul><li><p>Integrate ML models onto Qualcomm&rsquo;s Cloud AI ML stack from frameworks such as PyTorch , TensorFlow, and ONNX.
</p></li></ul></div><div><ul><li><p>Drive improvements in <b>model </b><b>throughput </b><b>, </b><b>latency </b><b>, and accuracy </b>, with clear trade ‑ off analysis.
</p></li></ul></div><div><ul><li><p>Build, test, and deploy scalable inference pipelines using serving frameworks such as vLLM , TGI, and Triton.
</p></li></ul></div><div><ul><li><p>Optimize workloads for LLM and GenAI models across both multi-SoC and multi-card architectures.
</p></li></ul></div></div><div><div><ul><li><p>Collaborate with engineering teams to analyze and refine training and inference for advanced deep learning applications.
</p></li></ul></div><div><ul><li><p>Identify bottlenecks across compute, memory, and runtime, and guide optimization strategies.
</p></li></ul></div><div><ul><li><p>Contribute to Qualcomm&rsquo;s Cloud AI GitHub repository and developer documentation, sharing technical best practices and solutions.
</p></li></ul></div><div><ul><li><p>Develop and integrate end-to-end ML application pipelines with customer frameworks and libraries.<br>
</p></li></ul></div><p></p><div><p><b>B) Customer </b>‑ <b>Facing Technical Engagement
</b></p></div><div><ul><li><p>Act as a <b>trusted technical advisor </b>for customers deploying AI workloads.
</p></li></ul></div><div><ul><li><p>Engage in <b>hardware sizing and architecture discussions </b>, aligning model requirements with infrastructure capabilities.
</p></li></ul></div><div><ul><li><p>Provide technical guidance on:
</p></li></ul></div><div><ul><li><p>AI model selection
</p></li></ul></div><div><ul><li><p>Deployment feasibility
</p></li></ul></div><div><ul><li><p>System architecture and performance expectations
</p></li></ul></div><div><ul><li><p>Lead discussions on <b>model capabilities and limitations </b>based on real customer use cases.<br>
</p></li></ul></div><p></p><div><p><b>C) Model&ndash;Infrastructure Alignment
</b></p></div><div><ul><li><p>Assess and evaluate AI model requirements and recommend <b>alternative model approaches </b>when necessary.
</p></li></ul></div><div><ul><li><p>Align model characteristics (latency, throughput, accuracy) with <b>accelerator and system capabilities </b>.
</p></li></ul></div><div><ul><li><p>Connect <b>model requirements </b>with:
</p></li></ul></div><div><ul><li><p>Memory constraints
</p></li></ul></div></div><div><div><ul><li><p>Accelerator architecture
</p></li></ul></div><div><ul><li><p>Scaling limitations
</p></li></ul></div><div><ul><li><p>Support customers in defining <b>model selection strategies based on deployment realities </b>.<br>
</p></li></ul></div><p></p><div><p><b>D) Performance &amp; Scalability Engineering
</b></p></div><div><ul><li><p>Evaluate performance characteristics of AI models in production scenarios, including:
</p></li></ul></div><div><ul><li><p>Throughput expectations
</p></li></ul></div><div><ul><li><p>Latency targets
</p></li></ul></div><div><ul><li><p>Concurrency behavior
</p></li></ul></div><div><ul><li><p>Guide architecture decisions around:
</p></li></ul></div><div><ul><li><p>Scaling strategies (horizontal vs vertical)
</p></li></ul></div><div><ul><li><p>Hardware deployment sizing
</p></li></ul></div><div><ul><li><p>Contribute to discussions on:
</p></li></ul></div><div><ul><li><p>Workload scalability limits
</p></li></ul></div><div><ul><li><p>Impact of model selection on system performance and efficiency
</p></li></ul></div><div><ul><li><p>Provide insights into <b>capacity planning and infrastructure optimization </b>.<br>
</p></li></ul></div><p></p><div><p><b>E) End </b>‑ <b>to </b>‑ <b>End AI Pipeline Design
</b></p></div><div><ul><li><p>Drive discussions around <b>end </b>‑ <b>to </b>‑ <b>end AI pipelines </b>, including:
</p></li></ul></div><div><ul><li><p>Multi ‑ model workflows (e.g., detection + tracking + recognition)
</p></li></ul></div></div><div><div><ul><li><p>Data preprocessing and post ‑ processing stages
</p></li></ul></div><div><ul><li><p>Guide decisions on video and data processing stacks, including:
</p></li></ul></div><div><ul><li><p>Video pipeline choices (e.g., FFMPEG vs GStreamer )
</p></li></ul></div><div><ul><li><p>Integration into inference pipelines
</p></li></ul></div><div><ul><li><p>Ensure pipelines are aligned with:
</p></li></ul></div><div><ul><li><p>Performance requirements
</p></li></ul></div><div><ul><li><p>Hardware capabilities
</p></li></ul></div><div><ul><li><p>Real ‑ time constraints<br>
</p></li></ul></div><p></p><div><p><b>F) Model Trade </b>‑ <b>off Analysis &amp; Validation
</b></p></div><div><ul><li><p>Highlight and explain trade ‑ offs between:
</p></li></ul></div><div><ul><li><p>Accuracy vs compatibility
</p></li></ul></div><div><ul><li><p>Model quality vs deployment feasibility
</p></li></ul></div><div><ul><li><p>Support decision ‑ making on:
</p></li></ul></div><div><ul><li><p>Model simplification vs performance gains
</p></li></ul></div><div><ul><li><p>Precision vs efficiency trade ‑ offs
</p></li></ul></div><div><ul><li><p>Lead or support <b>model capability validation </b>in deployment environments.
</p></li></ul></div><div><ul><li><p>Collaborate with customers to define:
</p></li></ul></div><div><ul><li><p>Inference assumptions
</p></li></ul></div><div><ul><li><p>Model sizing strategies for large ‑ scale workloads<br>
</p></li></ul></div></div><p></p><div><div><p><b>Required Qualifications
</b></p></div><div><ul><li><p>Bachelor&rsquo;s degree in Computer Science , Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
</p></li></ul></div><div><ul><li><p>10&ndash;15+ years of experience in:
</p></li></ul></div><div><ul><li><p>D eep learning model development or deployment experience on CPUs/GPUs/ASICs.
</p></li></ul></div><div><ul><li><p>Inference systems and optimization
</p></li></ul></div><div><ul><li><p>Data center or edge AI platforms
</p></li></ul></div><div><ul><li><p>Strong experience with:
</p></li></ul></div><div><ul><li><p>Model quantization and optimization techniques
</p></li></ul></div><div><ul><li><p>AI model frameworks (e.g., PyTorch , TensorFlow)
</p></li></ul></div><div><ul><li><p>Model deployment pipelines
</p></li></ul></div><div><ul><li><p>Excellent C/C++/Python programming and software design skills, including debugging, and performance analysis.
</p></li></ul></div><div><ul><li><p>Hands on expertise with Linux-based systems, low level software, drivers, and system bring up.
</p></li></ul></div><div><ul><li><p>Proven ability to analyze and optimize model performance in production environments.
</p></li></ul></div><div><ul><li><p>Solid understanding of:
</p></li></ul></div><div><ul><li><p>AI inference hardware constraints
</p></li></ul></div><div><ul><li><p>System level performance bottlenecks
</p></li></ul></div><div><ul><li><p>Strong communication skills and experience in customer facing technical roles.
</p></li></ul></div><div><ul><li><p>Willingness to travel for customer engagements and strategic reviews.<br>
</p></li></ul></div></div><p></p><div><div><p><b>Preferred Qualifications
</b></p></div><div><ul><li><p>Skilled in deploying models on platforms that use hardware accelerators for inference.
</p></li></ul></div><div><ul><li><p>Experienced with managing multi-model workflows and building real-time AI systems, including computer vision, video, and analytics projects.
</p></li></ul></div><div><ul><li><p>Knowledgeable about distributed inference methods and handling large-scale model deployments.
</p></li></ul></div><div><ul><li><p>Proficient in developing and maintaining video processing workflows and using relevant software frameworks.
</p></li></ul></div><div><ul><li><p>Deep understanding of how system-level decisions affect performance in actual deployment environments.
</p></li></ul></div><div><ul><li><p>Capable of simplifying complex technical ideas into straightforward, useful advice for clients.
</p></li></ul></div><div><ul><li><p>Hands-on experience running deep learning models on popular ML frameworks such as PyTorch , TensorFlow, ONNX
</p></li></ul></div><div><ul><li><p>Experience developing software solutions that run in Linux environments with containers and orchestration
</p></li></ul></div><div><ul><li><p>Experience with Source code and configuration management tools, Git knowledge is required.
</p></li></ul></div><div><ul><li><p>Customer-facing experience translating customer requirements into technical solutions (discovery, scoping, success criteria, and execution plans).
</p></li></ul></div><div><ul><li><p>Proven ability to build and deliver technical demos, proofs-of-concept, and reference applications for ML/GenAI workloads.
</p></li></ul></div><div><ul><li><p>Strong technical writing skills to produce customer-ready documentation (getting started guides, deployment runbooks, troubleshooting guides) and deliver partner training sessions.
</p></li></ul></div><div><ul><li><p>Experience driving issue triage and technical escalations with customers, coordinating across product , hardware, and software engineering teams to resolution.
</p></li></ul></div><div><ul><li><p>Excellent stakeholder management and communication skills: present complex technical concepts clearly to both engineering and non-engineering audiences.<br>
</p></li></ul></div></div><p></p><div><p><b>Why Join Qualcomm
</b></p></div><div><p>At Qualcomm, you&rsquo;ll work at the intersection of <b>AI silicon, system architecture, and real </b>‑ <b>world deployment </b>. You will engage directly with strategic customers, influence next ‑ generation AI data center platforms, and help define scalable, power ‑ efficient infrastructure for the AI era. This role provides a unique opportunity to shape both <b>technology direction and customer outcomes </b>, while working with world ‑ class engineering and product teams.
</p><p></p><p><b>What's on Offer
</b></p><p>Apart from working with great people, we offer the below:
</p><ul><li><p>Salary including housing &amp; transport allowance
</p></li><li><p>Stock (RSU's) and performance related bonus
</p></li><li><p>16 weeks fully paid Maternity Leave
</p></li><li><p>6 weeks fully paid Paternity Leave
</p></li><li><p>Employee stock purchase scheme
</p></li><li><p>Child Education Allowance
</p></li><li><p>Relocation and immigration support (if needed)
</p></li><li><p>Life and Medical Insurance
</p></li><li><p>Live+ Well Reimbursement for health and recreational membership fees
</p></li></ul></div><p></p><p><b>Minimum Qualifications:
</b></p><ul><li>Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 6+ years of Software Engineering or related work experience.
</li></ul>OR
<br>
Master's degree in Engineering, Information Systems, Computer Science, or related field and 5+ years of Software Engineering or related work experience.
<br>
OR
<br>
PhD in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience.
<br>
<br>
<ul><li>3+ years of work experience with Programming Language such as C, C++, Java, Python, etc.
</li></ul><p></p><p>*References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.
</p><p></p><div><p>Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here . Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).
</p></div><p></p><p>Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
</p><p></p><p><b>To all Staffing and Recruiting Agencies </b>: Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.
</p><p></p><p>If you would like more information about this role, please contact Qualcomm Careers .</p></div>

Datamatics Global Services Ltd

<div><b>Position Title : AI/ML Engineer &ndash; Agentic AI</b><br>
<b>Experience Required: </b>4&ndash;6 Years (Should be open to Travel)<br>
<b>Employment Type : </b>Full-Time - Riyadh - Onsite<br>
<br>
<b>Job Overview</b><br>
We are seeking a highly skilled AI/ML Engineer with hands-on experience in building intelligent AI systems, machine learning models, and Agentic AI solutions. The ideal candidate will have strong expertise in designing, developing, and deploying AI-powered applications leveraging LLMs, autonomous agents, orchestration frameworks, and modern ML techniques.<br>
The role requires a strong engineering mindset, practical AI implementation experience, and the ability to work across the full AI/ML lifecycle from experimentation to production deployment.<br>
<br>
<b>Key Responsibilities</b><ul><li>
Design, develop, and deploy AI/ML solutions for real-world business use cases.</li><li>
Build and optimize <b>Agentic AI systems</b> using LLMs, autonomous workflows, and multi-agent architectures.</li><li>
Develop machine learning models for prediction, classification, recommendation, NLP, and automation tasks.</li><li>
Work with orchestration frameworks such as LangChain, CrewAI, AutoGen, LangGraph, or similar technologies.</li><li>
Integrate AI agents with APIs, databases, enterprise systems, and third-party tools.</li><li>
Fine-tune, evaluate, and optimize Large Language Models (LLMs).</li><li>
Develop Retrieval-Augmented Generation (RAG) pipelines and vector search implementations.</li><li>
Collaborate with data engineers, software developers, and product teams to deliver scalable AI solutions.</li><li>
Perform prompt engineering, model experimentation, and performance optimization.</li><li>
Ensure AI solutions are production-ready, scalable, secure, and cost-efficient.</li><li>
Monitor model performance, drift, and continuous improvement processes.</li><li>
Stay updated with emerging AI, Generative AI, and Agentic AI trends and technologies.</li></ul><b>
Required Skills &amp; Expertise</b><ul><li>
Strong hands-on experience in <b>Machine Learning, Generative AI, and Agentic AI development</b>.</li><li>
Proficiency in Python and AI/ML development frameworks.</li><li>
Experience with:<ul><li>
LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks</li><li>
OpenAI, Anthropic, Gemini, or open-source LLMs</li><li>
RAG architectures and Vector Databases</li><li>
Prompt Engineering &amp; AI workflow automation</li></ul></li><li>
Solid understanding of:<ul><li>
NLP concepts</li><li>
ML algorithms and model evaluation</li><li>
Deep Learning fundamentals</li><li>
AI model deployment and inference optimization</li></ul></li><li>
Experience with ML libraries/frameworks such as:<ul><li>
PyTorch</li><li>
TensorFlow</li><li>
Scikit-learn</li><li>
Hugging Face Transformers</li></ul></li><li>
Experience with cloud platforms such as Azure, AWS, or GCP is preferred.</li><li>
Familiarity with Docker, APIs, CI/CD, and MLOps practices.</li><li>
Strong problem-solving and analytical skills.</li></ul><b>
Preferred Qualifications</b><ul><li>
Bachelor&rsquo;s degree in Computer Science, Artificial Intelligence, Data Science, or related field.</li><li>
Experience working on enterprise AI automation or AI assistant platforms is highly preferred.</li><li>
Exposure to AI governance, evaluation frameworks, and AI observability tools is a plus.</li></ul><br>
<p>
</p><p>tTAmIvfFVv</p></div>

AI/ML Engineer – Agentic AI

AI Platform & Inference Suite Engineer (Staff/Senior Staff level) - Riyadh, KSA

<div>
 <strong>
  <strong>
   Company:
   <br/>
   <br/>
  </strong>
 </strong>
 Qualcomm Middle East Information Technology Company LLC
 <br/>
 <br/>
 <strong>
  <strong>
   Job Area:
   <br/>
   <br/>
  </strong>
 </strong>
 Engineering Group, Engineering Group &gt; Software Engineering
 <br/>
 <br/>
 <strong>
  <strong>
   General Summary:
   <br/>
   <br/>
  </strong>
 </strong>
 <strong>
  About Us
  <br/>
  <br/>
 </strong>
 Qualcomm is enabling a world where everyone and everything can be intelligently connected. You interact with products and technologies made possible by Qualcomm every day, including 5G-enabled smartphones that double as pro-level cameras and gaming devices, smarter vehicles and cities, and the technology behind the smart, connected factories that manufactured your latest purchase. Qualcomm 5G and AI innovations are the power behind the connected intelligent edge. You&rsquo;ll find our technologies behind and inside the innovations that deliver significant value across multiple industries and to billions of people every day.
 <br/>
 <br/>
 Qualcomm is growing its presence in Riyadh and is hiring Data Centre Engineers to support our expanding infrastructure across the region.
 <br/>
 <br/>
 As Saudi Arabia accelerates its digital transformation under Vision 2030, Qualcomm is investing in world‑class computing and data centre capabilities to power AI, cloud, and advanced connectivity at scale. This is a unique opportunity to work in a fast‑growing technology hub, supporting critical environments and helping shape the future of data centre operations in the Kingdom and beyond.
 <br/>
 <br/>
 <strong>
  <strong>
   About The Role
   <br/>
   <br/>
  </strong>
 </strong>
 We are seeking a Full-Stack Machine Learning Engineer to join our team, bridging AI solutions development with AI platform engineering for Qualcomm&rsquo;s AI Inference Suite and rack-scale data center deployments. This role involves designing, delivering, and supporting end-to-end AI services, agentic workflows, and fine-tuning pipelines, while enabling lifecycle automation, orchestration, and observability for large-scale data center environments.
 <br/>
 <br/>
 You will bring a strong combination of full-stack engineering expertise, machine learning proficiency, and infrastructure knowledge to build robust, scalable AI systems.
 <br/>
 <br/>
 <strong>
  <strong>
   Key Responsibilities Will Include:
   <br/>
   <br/>
  </strong>
 </strong>
 <ul>
  <li>
   API Development &amp; Optimization: Build and optimize API serving layers for AI inference workloads, ensuring model and hardware efficiency.
  </li>
  <li>
   Agentic Workflows &amp; RAG Pipelines: Develop intelligent agents and retrieval-augmented generation workflows using frameworks such as LangChain and crew.ai.
  </li>
  <li>
   Model Lifecycle Management: Implement production-grade bring-your-own-model and fine-tuning flows, including dataset ingestion, orchestration, evaluation, and deployment.
  </li>
  <li>
   LLM Runtime Integration: Work with various LLM runtimes (e.g., vLLM, Dynamo, llm-d) and leverage inference optimization techniques.
  </li>
  <li>
   SDK &amp; Tooling Contributions: Contribute to AI Inference Suite SDKs (Python/TypeScript/Java/Rust), CLI tools, and reference applications.
  </li>
  <li>
   Cluster Management: Design and maintain AI cluster management software for provisioning, orchestration, and monitoring.
  </li>
  <li>
   Telemetry &amp; Observability: Integrate out-of-band management via Redfish/IPMI and in-band telemetry using Prometheus/OpenTelemetry.
  </li>
  <li>
   Infrastructure-as-Code: Develop workflows using MAAS, Terraform, and Ansible for bare-metal and containerized deployments.
  </li>
  <li>
   Kubernetes Orchestration: Enable Kubernetes/Helm-based orchestration for inference clusters and multi-tenancy.
  </li>
  <li>
   Monitoring &amp; Dashboards: Build dashboards for rack health, inventory, and SLA compliance.
  </li>
  <li>
   Continuous Innovation: Stay current with GenAI trends, rack-scale AI orchestration, and data center best practices.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  <strong>
   Minimum Qualifications
   <br/>
   <br/>
  </strong>
 </strong>
 <ul>
  <li>
   Bachelor&rsquo;s degree in Computer Science, Engineering, or related field.
  </li>
  <li>
   5+ years of software engineering experience; 3+ years in ML or HPC environments.
  </li>
  <li>
   Strong programming skills in Python, Rust/Go, and TypeScript, with solid software development fundamentals.
  </li>
  <li>
   Deep understanding of data structures and algorithms in distributed systems and high-performance computing contexts.
  </li>
  <li>
   Hands-on experience with Kubernetes, Helm, Prometheus/OpenTelemetry, and Ansible/Terraform.
  </li>
  <li>
   Practical experience with LLM runtimes, agent frameworks, and rack-scale orchestration.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  <strong>
   Preferred Qualifications
   <br/>
   <br/>
  </strong>
 </strong>
 <ul>
  <li>
   Master&rsquo;s degree in Computer Science, Machine Learning, or related field.
  </li>
  <li>
   Experience building inference and fine-tuning pipelines, as well as agentic workflows.
  </li>
  <li>
   Knowledge of data centre resource lifecycle management, out-of-band protocols (Redfish/IPMI), and MAAS/OpenStack.
  </li>
  <li>
   Exposure to scale-up data centre networking technologies (RoCE/RDMA/NVLink).
  </li>
  <li>
   Contributions to inference and GenAI model performance optimization.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  <strong>
   What's On Offer
   <br/>
   <br/>
  </strong>
 </strong>
 Apart from working with great people, we offer the below:
 <br/>
 <br/>
 <ul>
  <li>
   Salary including housing &amp; transport allowance
  </li>
  <li>
   Stock (RSU's) and performance related bonus
  </li>
  <li>
   16 weeks fully paid Maternity Leave
  </li>
  <li>
   6 weeks fully paid Paternity Leave
  </li>
  <li>
   Employee stock purchase scheme
  </li>
  <li>
   Child Education Allowance
  </li>
  <li>
   Relocation and immigration support (if needed)
  </li>
  <li>
   Life and Medical Insurance
  </li>
  <li>
   Live+ Well Reimbursement for health and recreational membership fees
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  <strong>
   Minimum Qualifications:
   <br/>
   <br/>
  </strong>
 </strong>
 <ul>
  <li>
   Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 OR
 <br/>
 <br/>
 Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience.
 <br/>
 <br/>
 OR
 <br/>
 <br/>
 PhD in Engineering, Information Systems, Computer Science, or related field.
 <br/>
 <br/>
 <ul>
  <li>
   2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc.
  </li>
  <li>
   References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).
 <br/>
 <br/>
 Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
 <br/>
 <br/>
 <strong>
  To all Staffing and Recruiting Agencies
 </strong>
 : Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.
 <br/>
 <br/>
 If you would like more information about this role, please contact Qualcomm Careers.
</div>


Full Stack Machine Learning Engineer (Datacentre AI Engineering) - Riyadh, KSA

<div><h2 class="jobSectionHeader"><b>Company:
</b></h2>Qualcomm Middle East Information Technology Company LLC
<p></p><h2 class="jobSectionHeader"><b>Job Area:
</b></h2>Engineering Group, Engineering Group &gt; Software Engineering
<p></p><p><b>General Summary:
</b></p><p></p><div><div><p><b>About Us
</b></p></div><div><p>Qualcomm is enabling a world where everyone and everything can be intelligently connected. You interact with products and technologies made possible by Qualcomm every day, including intelligent edge devices, next-generation computing platforms, and advanced AI solutions. Qualcomm&rsquo;s leadership in AI, high ‑ performance compute, and connectivity is driving innovation across cloud, edge, and data center environments - delivering scalable, power ‑ efficient platforms that power the next generation of intelligent infrastructure.<br>
</p></div><p></p><div><p><b>About the Role
</b></p></div><div><p>Qualcomm is seeking a <b>Machine Learning Operations &amp; Customer Support Engineer </b>within the Customer Engineering team to support strategic customers deploying AI inference workloads on advanced Qualcomm AI inference accelerator s . These accelerators utilize Qualcomm's expertise in hardware-accelerated AI to deliver high-performance, energy-efficient generative AI and computer vision inference solutions for modern data centers.
</p></div><p></p><div><p>This is a <b>customer-facing, production-critical role </b>focused on ensuring maximum system uptime, reliability, and performance, while resolving customer support cases within defined SLAs/KPIs. The role requires deep expertise across ML inference pipelines, systems troubleshooting , and data center operations, working closely with customers, internal engineering, and product teams.
</p></div><div><p>The i deal candidate will bring a strong foundation in ML model deployment, systems engineering, rack-scale management software, DevOps/ MLOps automation, and cross functional collaboration.<br>
</p></div><p></p><div><p><b>What You&rsquo;ll Do
</b></p></div><div><p><b>Customer Support &amp; SLA Ownership
</b></p></div><div><ul><li>Act as the primary technical escalation point for customer issues related to AI inference workloads
</li></ul></div><div><ul><li>Own end-to-end case management, ensuring resolution within agreed SLAs and KPIs
</li></ul></div><div><ul><li><p>Drive incident response, triage, and root cause analysis (RCA) .
</p></li></ul></div><div><ul><li><p>Provide timely and transparent communication to customers on issue status and resolution
</p></li></ul></div><div><ul><li><p>Maintain high levels of customer satisfaction and service reliability
</p></li></ul></div><p></p><div><p><b>Uptime, Reliability &amp; Operations
</b></p></div><div><ul><li><p>Ensure high availability and uptime of customer AI deployments (rack-scale systems)
</p></li></ul></div><div><ul><li><p>Monitor system health, performance metrics, and workload behavior
</p></li></ul></div></div><div><div><ul><li><p>Implement and manage failover, redundancy, and resiliency mechanisms
</p></li></ul></div><div><ul><li><p>Proactively identify risks and implement preventative actions
</p></li></ul></div><p></p><div><p><b>AI Inference Workload Support
</b></p></div><div><ul><li><p>Support deployment, optimization, and troubleshooting of ML inference pipelines
</p></li></ul></div><div><ul><li><p>Debug issues across model, runtime, system, and hardware layers
</p></li></ul></div><div><ul><li><p>Analyze model performance (latency, throughput, accuracy tradeoffs) in production
</p></li></ul></div><div><ul><li><p>Support frameworks such as PyTorch , TensorFlow, ONNX, and model conversion flows
</p></li></ul></div><div><ul><li><p>Assist in model optimization techniques (quantization, batching, compilation, runtime tuning)
</p></li></ul></div><p></p><div><p><b>System &amp; Infrastructure Engineering
</b></p></div><div><ul><li><p>Support bare-metal and virtualized environments for AI workloads
</p></li></ul></div><div><ul><li><p>Troubleshoot issues across Linux OS, drivers, firmware, and networking stack
</p></li></ul></div><div><ul><li><p>Support deployment and maintenance using Infrastructure as Code ( IaC ) and automation tools
</p></li></ul></div><div><ul><li><p>Work with DCIM tools and monitoring systems for infrastructure visibility
</p></li></ul></div><div><ul><li><p>Coordinate with hardware vendors for accelerator, server, and networking issues
</p></li></ul></div><p></p><div><p><b>Monitoring, Observability &amp; Automation
</b></p></div><div><ul><li><p>Implement and manage monitoring systems (logs, metrics, traces)
</p></li></ul></div><div><ul><li><p>Build dashboards for uptime, SLA adherence, performance, and utilization
</p></li></ul></div><div><ul><li><p>Automate repetitive operational tasks using scripts and workflows
</p></li></ul></div><div><ul><li><p>Establish and enforce runbooks and standard operating procedures (SOPs)
</p></li></ul></div><p></p><div><p><b>Cross-Functional Collaboration
</b></p></div></div><div><div><ul><li><p>Work closely with Customer Engineering, Product, Engineering, and Support teams
</p></li></ul></div><div><ul><li><p>Provide structured feedback to engineering for product improvements and defect resolution
</p></li></ul></div><div><ul><li><p>Support customer onboarding, deployment readiness, and operational handover
</p></li></ul></div><div><ul><li><p>Participate in customer reviews, escalations, and technical deep dives<br>
</p></li></ul></div><p></p><div><p><b>Required Qualifications
</b></p></div><div><ul><li><p>Bachelor&rsquo;s degree in Computer Science , Computer Engineering, Electrical Engineering, or related field
</p></li></ul></div><div><ul><li><p>10&ndash;15+ years of experience in ML operations, systems engineering, or customer support engineering
</p></li></ul></div><div><ul><li><p>Proven experience in customer-facing technical roles with SLA-driven support models
</p></li></ul></div><div><ul><li><p>Strong experience with AI/ML inference workloads in production environments
</p></li></ul></div><div><ul><li><p>Deep understanding of end-to-end ML inference pipelines
</p></li></ul></div><div><ul><li><p>Hands-on experience with Linux systems, system bring-up, drivers, and debugging tools
</p></li></ul></div><div><ul><li><p>Strong understanding of AI accelerator architecture and system bottlenecks
</p></li></ul></div><div><ul><li><p>Experience with model deployment, optimization, and performance tuning
</p></li></ul></div><div><ul><li><p>Experience with data center operations and rack-scale deployments
</p></li></ul></div><div><ul><li><p>Familiarity with bare-metal, virtualization, and containerization technologies (Docker, Kubernetes)
</p></li></ul></div><div><ul><li><p>Knowledge of networking concepts (TCP/IP, RDMA, storage systems)
</p></li></ul></div><div><ul><li><p>Experience with cloud and hybrid environments
</p></li></ul></div><div><ul><li><p>Experience with monitoring/observability tools (Prometheus, Grafana, ELK, etc.)
</p></li></ul></div><div><ul><li><p>Strong skills in incident management, RCA, and production operations
</p></li></ul></div></div><div><div><ul><li><p>Experience defining and tracking SLAs, KPIs, and operational metrics
</p></li></ul></div><div><ul><li><p>Proficiency in Python, Bash, or similar scripting languages
</p></li></ul></div><div><ul><li><p>Experience in automation, DevOps, and MLOps tooling
</p></li></ul></div><div><ul><li><p>Strong problem-solving and diagnostic skills
</p></li></ul></div><div><ul><li><p>Excellent communication and customer engagement skills
</p></li></ul></div><div><ul><li><p>Ability to operate in high-pressure, mission-critical environments
</p></li></ul></div><div><ul><li><p>High attention to detail with a focus on quality, reliability, and accountability
</p></li></ul></div><p></p><div><p><b>Preferred Qualifications
</b></p></div><div><ul><li><p>Experience with Qualcomm Cloud AI or similar AI accelerator platforms
</p></li></ul></div><div><ul><li><p>Experience supporting large-scale AI deployments (LLMs, CV pipelines, generative AI)
</p></li></ul></div><div><ul><li><p>Familiarity with inference runtimes ( TensorRT , ONNX Runtime, custom runtimes)
</p></li></ul></div><div><ul><li><p>Experience with CI/CD pipelines for ML deployment<br>
</p></li></ul></div><p></p><div><p><b>Why Join Qualcomm
</b></p></div><div><p>At Qualcomm, you&rsquo;ll work at the intersection of <b>AI silicon, system architecture, and real </b>‑ <b>world deployment </b>. You will engage directly with strategic customers, influence next ‑ generation AI data center platforms, and help define scalable, power ‑ efficient infrastructure for the AI era. This role provides a unique opportunity to shape both <b>technology direction and customer outcomes </b>, while working with world ‑ class engineering and product teams.
</p><p></p><p><b>What's on Offer
</b></p><p>Apart from working with great people, we offer the below:
</p><ul><li><p>Salary including housing &amp; transport allowance
</p></li><li><p>Stock (RSU's) and performance related bonus
</p></li><li><p>16 weeks fully paid Maternity Leave
</p></li><li><p>6 weeks fully paid Paternity Leave
</p></li><li><p>Employee stock purchase scheme
</p></li><li><p>Child Education Allowance
</p></li><li><p>Relocation and immigration support (if needed)
</p></li><li><p>Life and Medical Insurance
</p></li><li><p>Live+ Well Reimbursement for health and recreational membership fees
</p></li></ul></div></div><p></p><p><b>Minimum Qualifications:
</b></p><ul><li>Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience.
</li></ul>OR
<br>
Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.
<br>
OR
<br>
PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
<br>
<br>
<ul><li>2+ years of work experience with Programming Language such as C, C++, Java, Python, etc.
</li></ul><p></p><p>*References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.
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ML Operations & Customer Support Engineer, Staff/Senior Staff level - Riyadh, KSA

Foodics

<div>
 <strong>
  Who Are We❓
  <br/>
  <br/>
 </strong>
 We Are Foodics! a leading restaurant management ecosystem and payment tech provider. Founded in 2014 with headquarter in Riyadh and offices across 5 countries, including UAE, Egypt, Jordan and Kuwait. We are currently serving customers and partners in over 35 different countries worldwide. Our innovative products have successfully processed over 6 billion (yes, billion with a B) orders so far! making Foodics one of the most rapidly evolving SaaS companies to ever emerge from the MENA region. Also Foodics has achieved three rounds of funding, with the latest raising $170 million in the largest SaaS funding round in MENA, boosting its innovation capabilities to better serve business owners.
 <br/>
 <br/>
 <strong>
  The Job in a Nutshell💡
  <br/>
  <br/>
 </strong>
 You will lead the design, development, and deployment of ML/AI/GenAI models that power core Foodics products (e.g., pricing, personalization, fraud detection). You'll collaborate with Data Engineers, Product Managers, and Platform teams to deliver production-grade models with real impact.
 <br/>
 <br/>
 <strong>
  What Will You Do❓
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Own end-to-end ML model lifecycle: problem framing, data exploration, training, deployment, monitoring
  </li>
  <li>
   Design and develop scalable solutions using classical ML and GenAI techniques
  </li>
  <li>
   Implement MLOps best practices: versioning, reproducibility, monitoring, CI/CD for models
  </li>
  <li>
   Collaborate with squads and platform teams to ensure reusability and adherence to standards
  </li>
  <li>
   Mentor junior ML engineers and contribute to the internal ML knowledge base
  </li>
  <li>
   Integrate models with APIs and backend services as needed
  </li>
  <li>
   Embrace and enforce a "you build it, you run it" approach, owning the full lifecycle of ML models from development through monitoring and continuous improvement
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  What Are We Looking For❓
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   5+ years' experience in applied ML, AI, or data science
  </li>
  <li>
   Strong proficiency in Python and ML/AI libraries (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, HuggingFace Transformers)
  </li>
  <li>
   Experience with MLOps tools (e.g., MLflow, SageMaker) and managing versioning, testing, and observability
  </li>
  <li>
   Deep understanding of model development workflows including feature engineering, hyperparameter tuning, model evaluation, and A/B testing
  </li>
  <li>
   Deep understanding of statistical modeling, statistical inference, and the appropriate application of statistical tests (e.g., t-test, chi-square, ANOVA, regression analysis); ability to interpret results and communicate implications to both technical and non-technical audiences
  </li>
  <li>
   Proven track record of deploying ML models in production at scale
  </li>
  <li>
   Knowledge of ML best practices including bias mitigation, explainability (e.g., SHAP, LIME), and model monitoring for drift and fairness
  </li>
  <li>
   Strong understanding of data pipelines, experimentation, and model evaluation
  </li>
  <li>
   Familiarity working in a cloud-native environment (AWS preferred) with CI/CD, GitOps, and IaC tools (e.g., Terraform, CDK)
  </li>
  <li>
   Hands-on experience with GenAI / LLM integration (e.g., RAG, fine-tuning, embeddings, prompt engineering) and tools such as LangChain, LangGraph, or LlamaIndex
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  What We Offer You❗
  <br/>
  <br/>
 </strong>
 We believe you will love working at Foodics!
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 <br/>
 <ul>
  <li>
   We have an inclusive and diverse culture that encourages innovation
  </li>
  <li>
   We offer highly competitive compensation packages, including bonuses and the potential for shares
  </li>
  <li>
   We prioritize personal development and offer regular training and an annual learning stipend to tackle new challenges and grow your career in a hyper-growth environment
  </li>
  <li>
   Join a talented team of over 30 nationalities working in 14 countries, and gain valuable experience in an exciting industry
  </li>
  <li>
   We offer autonomy, mentoring, and challenging goals that create incredible opportunities for both you and the company
  </li>
 </ul>
</div>


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