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senior data engineer

application support engineer Jobs in Riyadh

application support engineer

senior software engineer

ai automation (llms, rag) engineer Jobs in Riyadh

ai automation (llms, rag) engineer

ai technical lead

Talents Arena

<p>We are seeking a Staff AI Engineer to serve as a Technical Lead and Lead Practitioner.</p><p>At the L4 level, you are the &ldquo;Master Builder.&rdquo; You are not just an architect who draws diagrams; you are an expert engineer capable of implementing the entire system end-to-end. From raw data ingestion pipelines, to complex model training loops, down to final API deployment and frontend integration, you understand&mdash;and can implement&mdash;every layer of the stack.</p><p>You will lead by example. You will architect scalable systems while also getting your hands dirty building them, ensuring the team consistently delivers excellence.</p><p>Key <b>Responsibilities</b></p><p><b>1. Full-System Implementation &amp; Ownership</b></p><ul><li>End-to-End Capability: Possess the technical ability to build the entire AI product. While leveraging the team for speed, you are capable of implementing every component yourself, from database schema design to the inference engine.</li><li>Lead Implementation: Code the most complex features and act as the technical backstop for the team. If a subsystem fails or a model fails to converge, you are the one who fixes it.</li><li>Holistic Engineering: Ensure the entire system works harmoniously. You don&rsquo;t just optimize the model&mdash;you optimize how the model interacts with the database, the network, and the user.</li></ul><p><b>2. Technical Architecture &amp; Strategy</b></p><ul><li>System Design: Design robust, scalable, and fault-tolerant AI architectures, ensuring all components built by the team fit together seamlessly.</li><li>Technical Roadmap: Translate vague business requirements into concrete, executable engineering tasks.</li><li>Standardization: Establish and enforce engineering standards, including coding styles, testing frameworks, and MLOps tools.</li></ul><p><b>3. Technical Leadership &amp; Force Multiplication</b></p><ul><li>Mentorship: Elevate Senior and Junior engineers. Code reviews focus not only on correctness, but also on teaching design patterns and architectural thinking.</li><li>Code Review Authority: Act as the standard-bearer for code quality, preventing careless technical debt and ensuring a clean, maintainable codebase.</li></ul><p><b>Qualifications &amp; Expertise</b></p><p>Experience</p><ul><li>8&ndash;12 years of total technical experience.</li><li>4+ years of deep, hands-on experience building production AI systems.</li><li>Proven track record of taking at least one complex AI product from concept to scale, owning the full technical lifecycle.</li></ul><p>Technical Competencies</p><ul><li>Full-Stack AI Fluency: Expert in Python (PyTorch/TensorFlow) with strong proficiency in the surrounding ecosystem, including SQL, API design, Docker, and cloud infrastructure. Able to build both the &ldquo;brain&rdquo; and the &ldquo;wrapper.&rdquo;</li><li>Architectural Vision: Ability to visualize the entire system and understand how decisions in the data pipeline impact frontend latency and user experience.</li><li>Deep Implementation Skills: Comfortable writing complex custom loss functions, optimizing CUDA kernels, and debugging distributed system race conditions.</li><li>Portfolio &amp; Presence: Sharing GitHub and Hugging Face accounts is highly preferred. Active repositories, open-source contributions, or published models are a strong plus.</li></ul><p>Professional Attributes</p><ul><li>Total Ownership: Take full responsibility for the success of the technical solution end-to-end.</li><li>Pragmatic Expert: Balance &ldquo;perfect code&rdquo; with &ldquo;shipping on time,&rdquo; without ever compromising system stability.</li></ul><p>Work Location: In person</p>

Staff AI Engineer

Master Works

<div>
 Seeking an experienced leader to manage and oversee strategic Data &amp; AI initiatives within a government, banking, or enterprise environment. The candidate will lead cross-functional teams, manage project delivery, and drive digital transformation initiatives aligned with business objectives.
 <br/>
 <br/>
 <strong>
  Key Responsibilities:
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Lead and manage Data &amp; AI projects and delivery operations
  </li>
  <li>
   Oversee project planning, execution, governance, risks, timelines, and reporting
  </li>
  <li>
   Manage multidisciplinary teams including Data Engineers, Data Scientists, Analysts, and Project Managers
  </li>
  <li>
   Ensure successful delivery of projects within scope, budget, and quality standards
  </li>
  <li>
   Collaborate with executive stakeholders and business leaders to align delivery with strategic goals
  </li>
  <li>
   Drive Agile methodologies and modern project delivery practices
  </li>
  <li>
   Manage vendors, partners, and external stakeholders
  </li>
  <li>
   Support enterprise digital transformation and data-driven initiatives
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Requirements
  <br/>
  <br/>
 </strong>
 <strong>
  Qualifications &amp; Experience:
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Saudi National
  </li>
  <li>
   Bachelor's degree in Computer Science, IT, Engineering, Data Science, or related field
  </li>
  <li>
   8+ years of experience in Project Management, Data Delivery, AI Programs, or Digital Transformation
  </li>
  <li>
   Previous experience within Banking, Government, or Large Enterprise environments is highly preferred
  </li>
  <li>
   Strong leadership and stakeholder management experience
  </li>
  <li>
   Proven knowledge of Agile frameworks and enterprise project delivery
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Certifications:
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   PMP
  </li>
  <li>
   PMI-ACP
  </li>
  <li>
   Scrum Master
  </li>
  <li>
   SAFe Agile
  </li>
  <li>
   ITIL
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Technical Expertise:
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Data Platforms &amp; Analytics
  </li>
  <li>
   AI &amp; Machine Learning Initiatives
  </li>
  <li>
   Agile Delivery Frameworks
  </li>
  <li>
   PMO Governance
  </li>
  <li>
   Digital Transformation
  </li>
  <li>
   Risk &amp; Vendor Management
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Core Competencies:
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Leadership &amp; Communication
  </li>
  <li>
   Strategic Thinking
  </li>
  <li>
   Delivery Management
  </li>
  <li>
   Problem Solving
  </li>
  <li>
   Stakeholder Management
  </li>
 </ul>
</div>


Data & AI Delivery, Head

MASTER-WORKS

<div><p>Seeking an experienced leader to manage and oversee strategic Data &amp; AI initiatives within a government, banking, or enterprise environment. The candidate will lead cross-functional teams, manage project delivery, and drive digital transformation initiatives aligned with business objectives.</p><p></p><p><b>
Key Responsibilities:
</b></p><ul><li>Lead and manage Data &amp; AI projects and delivery operations.</li><li>Oversee project planning, execution, governance, risks, timelines, and reporting.</li><li>Manage multidisciplinary teams including Data Engineers, Data Scientists, Analysts, and Project Managers.</li><li>Ensure successful delivery of projects within scope, budget, and quality standards.</li><li>Collaborate with executive stakeholders and business leaders to align delivery with strategic goals.</li><li>Drive Agile methodologies and modern project delivery practices.</li><li>Manage vendors, partners, and external stakeholders.</li><li>Support enterprise digital transformation and data-driven initiatives.</li></ul><p><br>
</p><br>
<br>
<b>Requirements</b><p><br>
</p><p><b>Qualifications &amp; Experience:</b></p><ul><li>Saudi National.</li><li>Bachelor&rsquo;s degree in Computer Science, IT, Engineering, Data Science, or related field.</li><li>8+ years of experience in Project Management, Data Delivery, AI Programs, or Digital Transformation.</li><li>Previous experience within Banking, Government, or Large Enterprise environments is highly preferred.</li><li>Strong leadership and stakeholder management experience.</li><li>Proven knowledge of Agile frameworks and enterprise project delivery.</li></ul><p></p><p><b>
Certifications:</b></p><ul><li>PMP</li><li>PMI-ACP</li><li>Scrum Master</li><li>SAFe Agile</li><li>ITIL</li></ul><p></p><p><b><br>
Technical Expertise:</b></p><ul><li>Data Platforms &amp; Analytics</li><li>AI &amp; Machine Learning Initiatives</li><li>Agile Delivery Frameworks</li><li>PMO Governance</li><li>Digital Transformation</li><li>Risk &amp; Vendor Management</li></ul><p></p><p><b>
Core Competencies:</b></p><ul><li>Leadership &amp; Communication</li><li>Strategic Thinking</li><li>Delivery Management</li><li>Problem Solving</li><li>Stakeholder Management</li></ul></div><p></p>

intelmatix

<div>
 <strong>
  About Intelmatix
  <br/>
  <br/>
 </strong>
 Intelmatix is a deep tech Artificial intelligence (AI) company founded in July 2021 by a group of MIT scientists with the vision of transforming enterprises to become cognitive. A cognitive enterprise is one that uses AI and Decision Intelligence in making their decisions. This leads to better business decisions with improved accuracy, reduced errors, and better outcomes across various aspects of the business.
 <br/>
 <br/>
 <strong>
  About The Role
  <br/>
  <br/>
 </strong>
 We are seeking a highly experienced Senior Data Engineer to lead the technical design, implementation, and delivery of an enterprise-grade, AI-ready Data Lakehouse. This role is critical to establishing the foundational data layer for a massive digital transformation initiative, designed to power advanced Artificial Intelligence (AI) agents, digital workers, and complex knowledge graphs (Ontologies).
 <br/>
 <br/>
 The ideal candidate will have a strong background in software development with a focus on building and optimizing data pipelines, ensuring data quality, and integrating data from various sources. As a Senior Data Engineer, you will play a key role in designing, developing, and maintaining scalable data infrastructure that supports our business intelligence and analytics efforts.
 <br/>
 <br/>
 Based in Riyadh, you will be responsible for architecting a highly secure platform that complies with strict national data residency and cybersecurity standards. You will not only build the system but also act as a technical leader, mentoring client engineering teams through a collaborative "co-build" model to ensure long-term operational ownership.
 <br/>
 <br/>
 Key Responsibilities
 <br/>
 <br/>
 <ul>
  <li>
   Lakehouse Architecture &amp; Implementation: Design and deploy a unified Data Lakehouse utilizing the Medallion architecture (Bronze, Silver, Gold) and open table formats (e.g., Delta Lake, Apache Iceberg) on cloud infrastructure hosted within Saudi Arabia.
  </li>
  <li>
   Data Ingestion &amp; Pipeline Engineering: Build reusable, automated ingestion frameworks (batch and streaming) capable of processing both structured data (RDBMS, APIs) and unstructured data (PDFs, policy documents) to feed downstream AI models and semantic reasoning engines.
  </li>
  <li>
   Data Quality &amp; Governance: Implement automated data quality "circuit breakers" (completeness, uniqueness, referential integrity) and end-to-end data lineage tracking frameworks.
  </li>
  <li>
   Optimization: Optimize data processing workflows for performance, scalability, and cost-efficiency.
  </li>
  <li>
   System Monitoring and Maintenance: Monitor and maintain data systems, responding to SEVs or other urgent issues to ensure continuous operations.
  </li>
  <li>
   Security &amp; Compliance: Ensure the platform adheres strictly to NCA (National Cybersecurity Authority) and NDMO (National Data Management Office) standards. Implement AES-256 encryption at rest, TLS 1.2+ in transit, robust Key Management Systems (KMS), and centralized audit logging.
  </li>
  <li>
   Access Control Integration: Design and deploy granular Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC), integrating seamlessly with existing enterprise Identity Providers (e.g., Active Directory).
  </li>
  <li>
   Capability Building &amp; Handover: Lead hands-on knowledge transfer sessions, pair-programming with client engineers, creating operational runbooks, and conducting "Game Day" failure simulations to ensure the client&rsquo;s team is fully ready to operate the platform independently.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Qualifications &amp; Experience
 </strong>
 <br/>
 <br/>
 <ul>
  <li>
   Education: Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Engineering, or a related field.
  </li>
  <li>
   Experience: 5+ years of proven experience in Data Engineering, Distributed Systems, or Big Data Architecture, with at least 2+ years specifically leading Data Lakehouse or Cloud Data Platform implementations.
  </li>
  <li>
   Technical Skills &amp; Core Technologies:
  </li>
  <ul>
   <li>
    Programming Languages: Proficiency in programming languages such as Python, Java, or Scala.
   </li>
   <li>
    Data Architecture &amp; System Design: Strong expertise in designing data-intensive applications, complex data modeling, and schema design for enterprise environments.
   </li>
   <li>
    Distributed Systems &amp; Lakehouse Technologies: Deep, hands-on experience with distributed processing engines (e.g., Apache Spark, Kafka, Hadoop) and modern open table formats (e.g., Delta Lake, Apache Iceberg, Apache Hudi).
   </li>
   <li>
    ETL/ELT &amp; Orchestration: Experience designing and building robust data pipelines using modern transformation and orchestration tools (e.g., Apache Airflow, Prefect, dbt).
   </li>
   <li>
    Database Ecosystems: Proven track record with relational databases (e.g., PostgreSQL, MySQL), NoSQL platforms (e.g., MongoDB, Cassandra), and distributed SQL query engines like Hive and Trino.
   </li>
   <li>
    Cloud Infrastructure: Proven experience deploying enterprise data solutions on major cloud providers, specifically within localized Saudi cloud regions. Expertise in Oracle Cloud Infrastructure (OCI) or Google Cloud Platform (GCP) is highly preferred, though experience with AWS or Azure is acceptable.
   </li>
  </ul>
  <li>
   Analytical Skills: Strong problem-solving skills with a keen eye for detail and a passion for data.
  </li>
  <li>
   AI/Data Science Enablement: Prior experience building data pipelines optimized for Machine Learning, Natural Language Processing (NLP), vector embeddings, or Knowledge Graphs/Ontologies is highly desirable.
  </li>
  <li>
   Security &amp; Networking: Strong understanding of enterprise network security, Private Endpoints, Identity &amp; Access Management (IAM), and cryptographic key management.
  </li>
  <li>
   Communication: Excellent written and verbal communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders.
  </li>
  <li>
   Leadership Skills: Demonstrated ability to lead technical teams, manage stakeholder expectations, and successfully transition complex systems to internal IT/Data teams.
  </li>
  <li>
   Regulatory Knowledge: Familiarity with Saudi Arabian data compliance frameworks (NCA CCC, NDMO, SDAIA) is highly preferred.
  </li>
 </ul>
</div>


Senior Data Engineer

Veeam Software

<div><div><div><p>Veeam is the Data and AI Trust Company, specializing in helping organizations ensure their data and AI are fully understood, secured, and resilient to enable the acceleration of safe AI at scale. As the market leader in both data resilience and data security posture management, Veeam is built for the convergence of identity, data, security, and AI risk. Headquartered in Seattle with offices in more than 30 countries, Veeam protects over 550,000 customers worldwide, who trust Veeam to keep their businesses running. Join us as we go fearlessly forward together, growing, learning, and making a real impact for some of the world&rsquo;s biggest brands.</p>
</div></div><div><p><b>
About the Role:</b></p>
<p>
Veeam, following its acquisition of Securiti AI - the leader in AI-powered data security posture management (DSPM) - is seeking a Senior Sales Engineer to drive technical leadership in our sales team, focusing on the Securiti AI portfolio. You will partner with a Securiti Sales Specialist and support 3&ndash;5 Account Executives and Veeam Solution Engineers as the technical subject matter expert.</p>
<p>
You will guide customers from needs assessment to solution design, delivering hands-on demos and proof-of-concepts to showcase value. Success in this role requires strong technical skills and the ability to build trust with clients.</p>
<p><b>
What You&rsquo;ll Do:</b></p>
<ul>
<li>Act as the primary technical contact for Securiti AI solutions during the sales cycle</li>
<li>Lead technical discussions and deliver customized security solutions</li>
<li>Address customer needs in data discovery, posture management, and compliance</li>
<li>Design and present product demonstrations and proof-of-concepts</li>
<li>Collaborate with product, engineering, and customer success teams to ensure smooth transitions</li>
</ul>
<p><b>What You&rsquo;ll Bring:</b></p>
<ul>
<li>Significant experience in pre-sales or sales engineering</li>
<li>Strong understanding of data and/or cloud security</li>
<li>Effective communicator with both technical and business stakeholders</li>
<li>10+ years technical sales experience in security</li>
<li>15+ years in Software/SaaS/Enterprise technical sales</li>
<li>Hands-on experience with AWS, Azure, or GCP</li>
<li>Knowledge of data privacy/security standards (e.g., GDPR, NIST 800-53, ISO 27001)</li>
<li>Experience with DSPM frameworks, Linux, Kubernetes, or cloud data systems</li>
<li>Relevant certifications (CISSP, CISM, or cloud-specific)</li>
</ul>
<p><b>What You&rsquo;ll Get:</b></p>
<p>
Veeam offers benefits that support your whole self:</p>
<ul>
<li>24 vacation days for first 2 years, increasing to 30 days after 2 years of service, plus 4 extra global VeeaMe Days for self-care and 24 paid volunteer hours annually through Veeam Cares</li>
<li>Private medical insurance for employees, spouses, and dependents</li>
<li>Life and accident insurance coverage for added financial protection</li>
<li>Free Employee Assistance Program (EAP) with up to 12 therapy sessions, plus legal and financial advice</li>
<li>Life and Disability insurance with AD&amp;D coverage equal to 3x annual salary.</li>
<li>Housing allowance to help cover rental costs</li>
<li>Education allowance for up to three dependent children</li>
<li>Annual flight allowance for employees and their families</li>
<li>Opportunities to learn and grow through on-demand libraries (LinkedIn Learning, O&rsquo;Reilly), mentoring, workshops, and learning events like our annual Global Day of Learning</li>
</ul>
<p>#LI-DF1 #LI-REMOTE</p></div><p></p><div>
<div><b>
Veeam Software is an equal opportunity employer</b> and does not tolerate discrimination in any form on the basis of race, color, religion, gender, age, national origin, citizenship, disability, veteran status or any other classification protected by federal, state or local law. All your information will be kept confidential.</div>
<div>
<p>Please note that any personal data collected from you during the recruitment process will be processed in accordance with our Recruiting Privacy Notice.</p>
<p>
The Privacy Notice sets out the basis on which the personal data collected from you, or that you provide to us, will be processed by us in connection with our recruitment processes.</p>
<p>
By applying for this position, you consent to the processing of your personal data in accordance with our Recruiting Privacy Notice.<br>
<br>
<b>By submitting your application, you acknowledge that the information provided in your job application and any supporting documents is complete and accurate to the best of your knowledge. Any misrepresentation, omission, or falsification of information may result in disqualification from consideration for employment or, if discovered after employment begins, termination of employment.</b></p></div></div></div><p></p>

Senior Sales Engineer - Data & AI Security

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>

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