machine learning infrastructure engineer

Glow Beauty On Demand

<br><b>Job Title<br></b>ML Platform Engineer   Generative AI &amp; Machine Learning Infrastructure<br>Job Description<br>ML Platform Engineer   Generative AI &amp; Machine Learning Infrastructure<br> <br>Location: Dubai, UAE<br>Industry: Technology / AI / Food &amp; Q-commerce<br>Function: Machine Learning Engineering<br>Gender: Any<br>Candidate Nationality: Any<br>Job Type: Full-time<br> <br><b>Role Overview<br></b>Talabat, the leading on-demand food and Q-commerce app in the region, is seeking an experienced ML Platform Engineer to design, develop, and scale robust machine learning and generative AI platforms. The role involves building end-to-end ML infrastructure, enabling fast experimentation, efficient model deployment, and seamless monitoring to support next-generation AI-driven solutions for millions of users.<br> <br><b>Key Responsibilities<br></b> <br>* Build scalable ML platforms supporting data ingestion, model training, deployment, and monitoring for both traditional ML and generative AI models<br>* Design standardized ML workflows using tools like MLflow, Kubeflow, and implement CI/CD pipelines with Docker and Kubernetes<br>* Optimize generative AI deployments involving transformers, embeddings, RAG systems, and vector databases such as Pinecone, Redis, or Weaviate<br>* Implement real-time serving frameworks like TensorFlow Serving, NVIDIA Triton, or Seldon for production-grade inference<br>* Automate model lifecycle management and implement observability measures to track performance, detect drift, and maintain reliability<br>* Collaborate with data engineering and product teams to align ML infrastructure with strategic business goals<br>* Drive cloud infrastructure integration using AWS or GCP services, including Kubernetes clusters, managed ML services, and serverless components<br>* Ensure cost efficiency, performance optimization, and adherence to best practices for MLOps and generative AI workflows<br> <br><b>Qualifications and Experience<br></b> <br>* Bachelor s degree in Computer Science, Engineering, or related field (Master s preferred)<br>* Minimum 3 years of experience in ML platform engineering, MLOps, or generative AI infrastructure roles<br>* Expertise in Python, ML frameworks (TensorFlow, PyTorch), and APIs such as Hugging Face and LangChain<br>* Hands-on experience with containerization, orchestration (Docker, Kubernetes), and infrastructure-as-code tools (Terraform, Helm)<br>* Proven ability to build real-time inference pipelines integrated with feature stores and streaming platforms like Kafka or Kinesis<br>* Familiarity with SQL for data processing and knowledge of observability tools for monitoring AI systems<br>* Strong understanding of model optimization techniques including quantization, batching, and prompt-tuning strategies<br><br>

ML Platform Engineer

<b><br>Job Title<br>ML Platform Engineer   Generative AI &amp; Machine Learning Infrastructure<br>Job Description<br>ML Platform Engineer   Generative AI &amp; Machine Learning Infrastructure<br> <br>Location: Dubai, UAE<br>Industry: Technology / AI / Food &amp; Q-commerce<br>Function: Machine Learning Engineering<br>Gender: Any<br>Candidate Nationality: Any<br>Job Type: Full-time<br></b> <br><b>Role Overview<br></b>Talabat, the leading on-demand food and Q-commerce app in the region, is seeking an experienced ML Platform Engineer to design, develop, and scale robust machine learning and generative AI platforms. The role involves building end-to-end ML infrastructure, enabling fast experimentation, efficient model deployment, and seamless monitoring to support next-generation AI-driven solutions for millions of users.<br> <br><b>Key Responsibilities<br></b> <br>* Build scalable ML platforms supporting data ingestion, model training, deployment, and monitoring for both traditional ML and generative AI models<br>* Design standardized ML workflows using tools like MLflow, Kubeflow, and implement CI/CD pipelines with Docker and Kubernetes<br>* Optimize generative AI deployments involving transformers, embeddings, RAG systems, and vector databases such as Pinecone, Redis, or Weaviate<br>* Implement real-time serving frameworks like TensorFlow Serving, NVIDIA Triton, or Seldon for production-grade inference<br>* Automate model lifecycle management and implement observability measures to track performance, detect drift, and maintain reliability<br>* Collaborate with data engineering and product teams to align ML infrastructure with strategic business goals<br>* Drive cloud infrastructure integration using AWS or GCP services, including Kubernetes clusters, managed ML services, and serverless components<br>* Ensure cost efficiency, performance optimization, and adherence to best practices for MLOps and generative AI workflows<br> <br><b>Qualifications and Experience<br></b> <br>* Bachelor s degree in Computer Science, Engineering, or related field (Master s preferred)<br>* Minimum 3 years of experience in ML platform engineering, MLOps, or generative AI infrastructure roles<br>* Expertise in Python, ML frameworks (TensorFlow, PyTorch), and APIs such as Hugging Face and LangChain<br>* Hands-on experience with containerization, orchestration (Docker, Kubernetes), and infrastructure-as-code tools (Terraform, Helm)<br>* Proven ability to build real-time inference pipelines integrated with feature stores and streaming platforms like Kafka or Kinesis<br>* Familiarity with SQL for data processing and knowledge of observability tools for monitoring AI systems<br>* Strong understanding of model optimization techniques including quantization, batching, and prompt-tuning strategies<br>

ML Platform Engineer Generative AI & Machine Learning Infrastructure

<br><b>Job Title<br></b>ML Platform Engineer   Generative AI &amp; Machine Learning Infrastructure<br><b>Job Description<br></b>ML Platform Engineer   Generative AI &amp; Machine Learning Infrastructure<br> <br><b>Location: Dubai, UAE<br>Industry: Technology / AI / Food &amp; Q-commerce<br>Function: Machine Learning Engineering<br>Gender: Any<br>Candidate Nationality: Any<br>Job Type: Full-time<br> <br>Role Overview</b><br>Talabat, the leading on-demand food and Q-commerce app in the region, is seeking an experienced ML Platform Engineer to design, develop, and scale robust machine learning and generative AI platforms. The role involves building end-to-end ML infrastructure, enabling fast experimentation, efficient model deployment, and seamless monitoring to support next-generation AI-driven solutions for millions of users.<br> <br><b>Key Responsibilities<br></b> <br>* Build scalable ML platforms supporting data ingestion, model training, deployment, and monitoring for both traditional ML and generative AI models<br>* Design standardized ML workflows using tools like MLflow, Kubeflow, and implement CI/CD pipelines with Docker and Kubernetes<br>* Optimize generative AI deployments involving transformers, embeddings, RAG systems, and vector databases such as Pinecone, Redis, or Weaviate<br>* Implement real-time serving frameworks like TensorFlow Serving, NVIDIA Triton, or Seldon for production-grade inference<br>* Automate model lifecycle management and implement observability measures to track performance, detect drift, and maintain reliability<br>* Collaborate with data engineering and product teams to align ML infrastructure with strategic business goals<br>* Drive cloud infrastructure integration using AWS or GCP services, including Kubernetes clusters, managed ML services, and serverless components<br>* Ensure cost efficiency, performance optimization, and adherence to best practices for MLOps and generative AI workflows<br> <br><b>Qualifications and Experience<br></b> <br>* Bachelor s degree in Computer Science, Engineering, or related field (Master s preferred)<br>* Minimum 3 years of experience in ML platform engineering, MLOps, or generative AI infrastructure roles<br>* Expertise in Python, ML frameworks (TensorFlow, PyTorch), and APIs such as Hugging Face and LangChain<br>* Hands-on experience with containerization, orchestration (Docker, Kubernetes), and infrastructure-as-code tools (Terraform, Helm)<br>* Proven ability to build real-time inference pipelines integrated with feature stores and streaming platforms like Kafka or Kinesis<br>* Familiarity with SQL for data processing and knowledge of observability tools for monitoring AI systems<br>* Strong understanding of model optimization techniques including quantization, batching, and prompt-tuning strategies<br> <br><b>What We Offer<br></b> <br>* Opportunity to work on advanced ML and AI projects impacting millions of users in the Middle East<br>* Competitive salary and benefits package<br>* A collaborative, innovation-driven environment with cutting-edge tools and technologies<br>* Career development in one of the fastest-growing tech ecosystems in the region<br>

ML Platform Engineer Generative AI & Machine Learning

Deeplight

<div><p>DeepLight AI is a specialist AI and data consultancy with extensive experience implementing intelligent enterprise systems across multiple industries, with particular depth in financial services and banking. Our team combines deep expertise in data science, statistical modeling, AI/ML technologies, workflow automation, and systems integration with a practical understanding of complex business operations.</p><p>
The AI Platform Engineer helps design, build and maintain the on-premises AI Infrastructure for our client, a market leader in the environment sector. The engineer plays a key role in enabling large-scale machine learning and generative AI workloads by developing robust, scalable, and secure platform solutions that support data scientists and ML engineers across the organisation.</p><p>
The role requires deep <b>Kubernetes and infrastructure automation experience</b> to optimise performance, streamline deployments, and ensure reliability in an on-prem environment.</p><p><b>
Requirements</b></p><p></p><ul>
<li>
Design, deploy, and manage on-premises Kubernetes clusters for AI and ML workloads.</li>
<li>Develop and maintain infrastructure-as-code using tools like Terraform, Helm, or Ansible</li>
<li>Build and optimise AI/ML pipelines and MLOps workflows for model training, deployment, and monitoring.</li>
<li>Collaborate with data science and engineering teams to deliver high-performance computing environments for large model training and inference.</li>
<li>Implement resource management, observability, and scaling strategies for GPU-based workloads.</li>
<li>Manage containerisation, networking, and storage solutions for AI workloads.</li>
<li>Ensure security, compliance, and reliability of the AI platform.</li>
<li>Automate operational processes and continuously improve platform efficiency.</li>
</ul><p></p><p><b>
What we need from you:</b></p><p></p><ul>
<li>
Proven experience managing Kubernetes clusters on-premises (not just cloud-managed solutions).</li>
<li>Experience working in a Consultancy capacity.</li>
<li>Strong background in container orchestration, CI/CD, and automation.</li>
<li>Proficiency in Linux administration, Docker, and networking fundamentals.</li>
<li>Hands-on experience with infrastructure-as-code (Terraform, Helm, Ansible, etc.).</li>
<li>Experience supporting AI/ML workloads (e.g., TensorFlow, PyTorch, Hugging Face, Ray, Kubeflow).</li>
<li>Familiarity with GPU scheduling and resource management on Kubernetes.</li>
<li>Knowledge of monitoring and logging tools (Prometheus, Grafana, ELK, etc.).</li>
<li>Strong scripting skills (Python, Bash, or Go).</li>
<li>Understanding of DevOps and MLOps best practices.</li>
</ul><p><b>
Benefits</b></p><p><b>
Benefits &amp; Growth Opportunities:</b></p><ul><li>Competitive salary and performance bonuses</li><li>Comprehensive health insurance</li><li>Professional development and certification support</li><li>Opportunity to work on cutting-edge AI projects</li><li>International exposure and travel opportunities</li><li>Flexible working arrangements</li><li>Career advancement opportunities in a rapidly growing AI company</li></ul><p>
This position offers a unique opportunity to shape the future of AI implementation while working with a talented team of professionals at the forefront of technological innovation. The successful candidate will play a crucial role in driving our company's success in delivering transformative AI solutions to our clients.</p></div>

AI Platform Engineer

General Motors

<div><p><b>Job Description
</b></p><div><div><p>Principal AI/ML Engineer, AV ML Infra
</p></div><div><p>We&rsquo;re General Motors (GM), a company driving the future of mobility with advanced self-driving and electric vehicle technologies.
</p></div><div><p>We&rsquo;re building the world&rsquo;s most innovative autonomous vehicles to safely connect people to the places, things, and experiences they care about. We believe self-driving vehicles will help save lives, reshape cities, give back time in transit, and restore freedom of movement for many.
</p></div><div><p>GM employees have the opportunity to grow and develop while learning from leaders at the forefront of their fields. With a culture of internal mobility, there&rsquo;s an opportunity to thrive in a variety of disciplines. This is a place for dreamers and doers to succeed.
</p></div><div><p>If you are looking to play a part in making a positive impact in the world by advancing the revolutionary work of self-driving vehicles, join us.
</p></div><div><p><b>About the team:
</b></p></div><div><p>The AV ML Infra team at GM builds ML infrastructure designed to meet the unique demands of AI and ML innovation, supporting a wide range of use cases across teams such as Embodied AI, Simulation, Data Science, and more. We enable scalable and efficient ML experimentation, enhance the productivity of ML engineers, and drive the adoption of cutting-edge ML techniques.
</p></div><div><p>Our ML infrastructure includes:
</p></div><div><ul><li><p><b>AI Validation &amp; Inference: </b>Ensures robust model performance by running large-scale simulation workloads and managing reliable ML inference pipelines.<br>
<br>
</p></li></ul></div><div><ul><li><p><b>ML Compute: </b>Streamlines and optimizes large-scale ML training and inference across cloud and on-prem compute resources.<br>
<br>
</p></li></ul></div><div><ul><li><p><b>AV Pipelines &amp; Lineage: </b>Automates ML workflows while tracking data and model lineage across diverse infrastructures, accelerating engineering velocity and ensuring reproducibility.
</p></li></ul></div><div><p>Together, these tools and systems empower GM to tackle the complexities of autonomous driving technology and expedite our path to commercialization.
</p></div><div><p><b>Position Overview:
</b></p></div><div><p>The Principal AI/ML Engineer will lead a growing organization, guiding the AV ML Infra team in achieving its mission while shaping a long -term vision and execution strategies across GM&rsquo;s AI and ML efforts. This leadership role will drive a transformative leap in our infrastructure capabilities to meet the scale we anticipate . The leader will articulate a clear vision and strategy, design an organizational structure to enable effective execution, and cultivate strong partnerships across cross-functional teams and stakeholders.
</p></div><div><p><i>Note: This role is part of an ML infrastructure engineering team and does not involve applying machine learning models for specific tasks. The focus is on developing infrastructure products that empower GM teams to perform machine learning and data science at </i><i>scale </i><i>.
</i></p></div><div><p><b>What </b><b>you&rsquo;ll </b><b>be doing:
</b></p></div><div><ul><li><p><b>Design &amp; Implementation: </b>Utilize the latest cloud technologies (GCP/Azure) to design, implement, and test scalable distributed computing and data processing solutions in the cloud.
</p></li></ul></div><div><ul><li><p><b>Project Ownership: </b>Take ownership of technical projects from inception to completion, contribute to the product roadmap, and make informed decisions on major technical trade-offs.
</p></li></ul></div><div><ul><li><p><b>Collaboration: </b>Engage effectively in team planning, code reviews, and design discussions, considering the impact of projects across multiple teams while proactively managing conflicts.
</p></li></ul></div><div><ul><li><p><b>Mentorship &amp; Recruitment: </b>Conduct technical interviews with calibrated standards, onboard, and mentor engineers and interns, fostering a culture of growth and knowledge sharing.
</p></li></ul></div></div><div><div><p><b>What you must have:
</b></p></div><div><ul><li><p>10+ years of experience, with a strong background in large-scale distributed systems preferred.
</p></li></ul></div><div><ul><li><p>5+ years of experience leading and driving large-scale initiatives.
</p></li></ul></div><div><ul><li><p>Proficiency in building scalable infrastructure on the cloud using Python, C++, Golang, or similar languages.
</p></li></ul></div><div><ul><li><p>Experience working with relational and NoSQL databases.
</p></li></ul></div><div><ul><li><p>Demonstrated ability to develop and maintain systems at scale.
</p></li></ul></div><div><ul><li><p>A Bachelor&rsquo;s, Master&rsquo;s, or Ph.D. in Computer Science, Electrical Engineering, Mathematics, Physics, or a related field; or equivalent practical experience.
</p></li></ul></div><div><ul><li><p>A passion for autonomous vehicle technology and its transformative potential.
</p></li></ul></div><div><ul><li><p>Strong attention to detail and a commitment to accuracy.
</p></li></ul></div><div><ul><li><p>A proven track record of efficiently solving complex problems.
</p></li></ul></div><div><ul><li><p>A startup mentality with a willingness to embrace uncertainty and wear multiple hats.
</p></li></ul></div><div><p><b>Bonus Points:
</b></p></div><div><ul><li><p>Experience with Google Cloud Platform, Microsoft Azure, or Amazon Web Services.
</p></li></ul></div><div><ul><li><p>Experience with open-source orchestration platforms such as Kubeflow, Flyte, Airflow, etc.
</p></li></ul></div><div><ul><li><p>Experience with Kubernetes.
</p></li></ul></div><div><ul><li><p>Understanding of Machine Learning (ML) models/pipelines.
</p></li></ul></div><div><ul><li><p>Python/C++/Golang proficiency .
</p></li></ul></div><div><ul><li><p>Relevant publications.<br>
<br>
<br>
</p></li></ul></div></div><p></p>&amp;#xa;&amp;#xa;&amp;#xa;&amp;#xa;
<p><b>About GM
</b></p><p>Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.
</p><p></p><p><b>Why Join Us
</b></p><p>We believe we all must make a choice every day &ndash; individually and collectively &ndash; to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.
</p><p></p><p><b>Benefits Overview
</b></p><p>From day one, we're looking out for your well-being&ndash;at work and at home&ndash;so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting <b>Total Rewards resources </b>.
</p><p></p><p><b>Non-Discrimination and Equal Employment Opportunities (U.S.)
</b></p><p>General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.
</p><p></p><p>All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.
</p><p></p><p>We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit <b>How we Hire </b>.
</p><p></p><p><b>Accommodations
</b></p><p>General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, <b>email </b>us or call us at 800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.</p></div><p></p>

Principal Staff AI/ML Engineer

Sumo Logic

<div><h2 class="jobSectionHeader"><b>Senior Machine Learning Engineer I</b></h2>
<p>
The proliferation of machine log data has the potential to give organizations unprecedented real-time visibility into their infrastructure and operations. With this opportunity comes tremendous technical challenges around ingesting, managing, and understanding high-volume streams of heterogeneous data</p>
<p>
As a <b>Machine Learning Engineer</b>, you'll build the intelligence behind the next generation of <b>agentic AI systems</b> that reason over massive, heterogeneous log data. You'll combine machine learning, prompt engineering, and rigorous evaluation to create autonomous AI agents that help organizations understand and act on their data in real time.</p>
<p>
You'll be part of a small, high-impact team shaping how AI agents understand complex machine data. This is an opportunity to work on cutting-edge LLM infrastructure and contribute to defining best practices in context engineering and AI observability.</p>
<h4 class="jobSectionHeader"><b>
Responsibilities</b></h4>
<ul><li><b>Design, implement, and optimize</b> agentic AI components including context engineering, memory management, and prompts.</li>
<li><b>Develop and maintain</b> golden datasets by defining sourcing strategies, working with data vendors, and ensuring quality and representativeness at scale.</li>
<li><b>Prototype and evaluate</b> novel prompting strategies and reasoning chains for model reliability and interpretability.</li>
<li><b>Collaborate cross-functionally</b> with product, data, and infrastructure teams to deliver end-to-end AI-powered insights.</li>
<li><b>Operate autonomously</b> in a fast-paced, ambiguous environment - defining scope, setting milestones, and driving outcomes.</li>
<li><b>Ensure reliability, performance, and observability</b> of deployed agents through rigorous testing and continuous improvement.</li>
<li><b>Maintain a strong bias for action</b>&mdash;delivering incremental, well-tested improvements that directly enhance customer experience.</li>
</ul>
<p><b>Required Qualifications</b></p>
<ul>
<li>B.Tech, M.Tech, or Ph.D. in Computer Science, Data Science, or a related field.</li>
<li>4-6 years of hands-on industry experience with demonstrable ownership and delivery.</li>
<li>Strong understanding of machine learning fundamentals, data pipelines, and model evaluation.</li>
<li><b>Proficiency in Python</b> and ML/data libraries (NumPy, pandas, scikit-learn); familiarity with JVM languages is a plus.</li>
<li>Working knowledge of <b>LLM core concepts</b>, prompt design, and <b>agentic design patterns</b>.</li>
<li>Strong communication skills and a passion for shaping emerging AI paradigms.</li>
</ul>
<p><b>Desired Qualifications</b></p>
<ul>
<li>Prior experience <b>building and deploying AI agents</b> or LLM applications in production.</li>
<li>Familiarity with modern <b>agentic AI frameworks</b> (e.g., LangGraph, LangChain, CrewAI).</li>
<li>Experience with <b>ML infrastructure and tooling</b> (PyTorch, MLflow, Airflow, Docker, AWS).</li>
<li>Exposure to <b>LLM Ops</b> - infrastructure optimization, observability, latency, and cost monitoring.</li>
</ul><h2 class="jobSectionHeader"><b>About Us</b></h2>
<p>
Sumo Logic, Inc. empowers the people who power modern, digital business. Sumo Logic enables customers to deliver reliable and secure cloud-native applications through its Sumo Logic SaaS Analytics Log Platform, which helps practitioners and developers ensure application reliability, secure and protect against modern security threats, and gain insights into their cloud infrastructures. Customers worldwide rely on Sumo Logic to get powerful real-time analytics and insights across observability and security solutions for their cloud-native applications. For more information, visit www.sumologic.com.</p></div>

Senior Machine Learning Engineer

<div><h2 class="jobSectionHeader"><b>Machine Learning Engineer</b></h2>
<p>
The proliferation of machine log data has the potential to give organizations unprecedented real-time visibility into their infrastructure and operations. With this opportunity comes tremendous technical challenges around ingesting, managing, and understanding high-volume streams of heterogeneous data</p>
<p>
As a <b>Machine Learning Engineer</b>, you'll build the intelligence behind the next generation of <b>agentic AI systems</b> that reason over massive, heterogeneous log data. You'll combine machine learning, prompt engineering, and rigorous evaluation to create autonomous AI agents that help organizations understand and act on their data in real time.</p>
<p>
You'll be part of a small, high-impact team shaping how AI agents understand complex machine data. This is an opportunity to work on cutting-edge LLM infrastructure and contribute to defining best practices in context engineering and AI observability.</p>
<h4 class="jobSectionHeader"><b>
Responsibilities</b></h4>
<ul><li><b>Design, implement, and optimize</b> agentic AI components including context engineering, memory management, and prompts.</li>
<li><b>Develop and maintain</b> golden datasets by defining sourcing strategies, working with data vendors, and ensuring quality and representativeness at scale.</li>
<li><b>Prototype and evaluate</b> novel prompting strategies and reasoning chains for model reliability and interpretability.</li>
<li><b>Collaborate cross-functionally</b> with product, data, and infrastructure teams to deliver end-to-end AI-powered insights.</li>
<li><b>Operate autonomously</b> in a fast-paced, ambiguous environment - defining scope, setting milestones, and driving outcomes.</li>
<li><b>Ensure reliability, performance, and observability</b> of deployed agents through rigorous testing and continuous improvement.</li>
<li><b>Maintain a strong bias for action</b>&mdash;delivering incremental, well-tested improvements that directly enhance customer experience.</li>
</ul>
<p><b>Required Qualifications</b></p>
<ul>
<li>B.Tech, M.Tech, or Ph.D. in Computer Science, Data Science, or a related field.</li>
<li>2-4 years of hands-on industry experience with demonstrable ownership and delivery.</li>
<li>Strong understanding of machine learning fundamentals, data pipelines, and model evaluation.</li>
<li><b>Proficiency in Python</b> and ML/data libraries (NumPy, pandas, scikit-learn); familiarity with JVM languages is a plus.</li>
<li>Working knowledge of <b>LLM core concepts</b>, prompt design, and <b>agentic design patterns</b>.</li>
<li>Strong communication skills and a passion for shaping emerging AI paradigms.</li>
</ul>
<p><b>Desired Qualifications</b></p>
<ul>
<li>Prior experience <b>building and deploying AI agents</b> or LLM applications in production.</li>
<li>Familiarity with modern <b>agentic AI frameworks</b> (e.g., LangGraph, LangChain, CrewAI).</li>
<li>Experience with <b>ML infrastructure and tooling</b> (PyTorch, MLflow, Airflow, Docker, AWS).</li>
<li>Exposure to <b>LLM Ops</b> - infrastructure optimization, observability, latency, and cost monitoring.</li>
</ul><h2 class="jobSectionHeader"><b>About Us</b></h2>
<p>
Sumo Logic, Inc. empowers the people who power modern, digital business. Sumo Logic enables customers to deliver reliable and secure cloud-native applications through its Sumo Logic SaaS Analytics Log Platform, which helps practitioners and developers ensure application reliability, secure and protect against modern security threats, and gain insights into their cloud infrastructures. Customers worldwide rely on Sumo Logic to get powerful real-time analytics and insights across observability and security solutions for their cloud-native applications. For more information, visit www.sumologic.com.</p></div>

Machine Learning Engineer

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