machine learning infrastructure engineer

Glow Beauty On Demand

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

ML Platform Engineer

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

ML Platform Engineer Generative AI & Machine Learning Infrastructure

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

ML Platform Engineer Generative AI & Machine Learning

Tesla

<div>What to Expect<div>
As a Software Engineer within the Autopilot AI Infrastructure team, you will work on reinforcing, optimizing, and scaling our infrastructure components supporting AI research activities for Autopilot and the Optimus.
At the core of our autonomy capabilities are neural networks that the research team is designing to train on very large amounts of data, across large-scale GPU clusters and our supercomputer Dojo. Robustly training these models at scale and in the shortest amount of time is critical to our mission.
We are building out the Machine Learning Platform that our engineers and leadership use to schedule, manage and monitor machine learning experiments, data pipelines and artifacts. With the ever-increasing size of our datasets and compute clusters, we are looking for an experienced backend engineer to help drive scalability improvements and new capabilities in the platform.</div> 
What You'll Do<div><ul><li>
Develop and deploy solutions to scale our infrastructure effectively in response to rapidly growing demands</li><li>
Drive implementation of best practices and monitoring systems to proactively detect and address issues in our production environment</li><li>
Work across the stack on tools and infrastructure empowering the machine learning team to be effective. This ranges from developing/running model training and evaluation code to back-end infrastructure to occasional front-end work</li><li>
Coordinate required resources with the team managing the cluster hardware to maintain high availability</li><li>
Work closely with the research team to understand requirements and priorities</li></ul></div> 

What You'll Bring<div><ul><li>
Expertise in designing scalable and durable distributed systems</li><li>
Strong knowledge of Python/Go and Linux</li><li>
Experience working with diverse backend infrastructure components (SQL / NoSQL databases, caching, message brokers, event streams, monitoring etc.)</li><li>
Hands-on experience with containerization and orchestration technologies (Docker, Kubernetes) and setting up CI/CD flows</li><li>
Knowledge of front-end development in React / strong product sense</li><li>
Knowledge of machine learning, computer vision, or neural networks</li><li>
Experience working with HPC clusters</li></ul></div> 

Compensation and Benefits<div>
Benefits</div><div>
Along with competitive pay, as a full-time Tesla employee, you are eligible for the following benefits at day 1 of hire:
<ul><li>Aetna PPO and HSA plans &gt; 2 medical plan options with $0 payroll deduction</li>
<li>Family-building, fertility, adoption and surrogacy benefits</li>
<li>Dental (including orthodontic coverage) and vision plans, both have options with a $0 paycheck contribution</li>
<li>Company Paid (Health Savings Account) HSA Contribution when enrolled in the High Deductible Aetna medical plan with HSA</li>
<li>Healthcare and Dependent Care Flexible Spending Accounts (FSA)</li>
<li>401(k) with employer match, Employee Stock Purchase Plans, and other financial benefits</li>
<li>Company paid Basic Life, AD&amp;D, short-term and long-term disability insurance</li>
<li>Employee Assistance Program</li>
<li>Sick and Vacation time (Flex time for salary positions), and Paid Holidays</li>
<li>Back-up childcare and parenting support resources</li>
<li>Voluntary benefits to include: critical illness, hospital indemnity, accident insurance, theft &amp; legal services, and pet insurance</li>
<li>Weight Loss and Tobacco Cessation Programs</li>
<li>Tesla Babies program</li>
<li>Commuter benefits</li>
<li>Employee discounts and perks program</li>
</ul></div><div>Expected Compensation</div><div>
$132,000 - $390,000/annual salary + cash and stock awards + benefits
Pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The total compensation package for this position may also include other elements dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.</div> 
Tesla is an Equal Opportunity / Affirmative Action employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state or local laws. 
 
Tesla is also committed to working with and providing reasonable accommodations to individuals with disabilities. Please let your recruiter know if you need an accommodation at any point during the interview process.</div>

Backend Software Engineer, Machine Learning Platform, AI Infrastructure

Millennium Management

<div>AI Platform Engineer
We&rsquo;re seeking an AI Platform Engineer to contribute to the development and management of cutting-edge AI infrastructure for our complex enterprise environment. In this role, you&rsquo;ll harness your expertise to build infrastructure which enables your team and the firm to manage autonomous agents, machine learning systems and other applications in the AI domain.
You&rsquo;ll design and manage federated AI ecosystems, including MCP servers, agents and enterprise AI tooling, while orchestrating agent deployment across distributed systems and service buses. This role requires technical depth, strong communication, and a passion for building transformative AI infrastructure.
If you&rsquo;re ready to shape the future of AI and thrive in a fast-paced, innovative environment, we want to hear from you!
Key Responsibilities:<ul><li>
Design and implement AI infrastructure to support federated agent development and distributed deployment across the organization.</li><li>
Manage and orchestrate AI agents to generate architecturally sound code, ensuring adherence to enterprise standards and best practices.</li><li>
Build and maintain MCP (Model Context Protocol) servers and related infrastructure for agent communication, coordination, and enterprise service bus integration.</li><li>
Accelerate code review processes by leveraging AI agents for automated analysis, quality checks, and architectural validation.</li><li>
Establish robust monitoring, governance, and security frameworks for AI agent operations in enterprise environments.</li><li>
Implement and manage secure authentication and authorization systems for MCP servers, including OAuth flows and API security.</li><li>
Collaborate with development teams to integrate AI-powered tools into existing workflows and CI/CD pipelines.</li><li>
Act as a deployed engineer, traveling to different teams across the firm to teach AI infrastructure best practices and guide adoption.</li><li>
Provide technical leadership in transitioning traditional development practices to AI-augmented approaches.</li></ul>
Qualifications:<ul><li>
Experience: 8+ years of professional software development experience with a demonstrated ability to architect and build scalable systems.</li><li>
AI Infrastructure: Experience with AI/ML infrastructure, agent orchestration, or similar AI-powered development tools.</li><li>
Programming Skills: Proficiency in Python, TypeScript/JavaScript, and experience with AI/ML frameworks and libraries.</li><li>
Enterprise Architecture: Strong understanding of enterprise software architecture patterns and distributed systems design.</li><li>
Cloud Platforms: Experience with cloud platforms (AWS, Azure, or GCP) and infrastructure-as-code tools.</li><li>
Containerization: Experience with Docker, Kubernetes, and container orchestration in production environments.</li><li>
API Design: Experience building and managing APIs, microservices, and event-driven architectures.</li><li>
Security: Strong understanding of information security principles, OAuth 2.0/OpenID Connect flows, API security, and secure system design.</li><li>
Communication: Strong communication and teaching skills with the ability to work as a deployed engineer across different teams.</li><li>
Education: Bachelor's or Master's degree in Computer Science, Engineering, or related technical field.</li></ul>
Preferred Qualifications:<ul><li>
Experience with AI agent frameworks, LLM integration, or Model Context Protocol (MCP) implementations.</li><li>
Background in building federated systems or multi-tenant AI platforms.</li><li>
Experience with AI governance, security, and compliance in enterprise environments.</li><li>
Background in information security, penetration testing, or security architecture roles.</li><li>
Familiarity with code generation tools, automated testing frameworks, and AI-powered development workflows.</li><li>
Knowledge of vector databases, embedding systems, and retrieval-augmented generation (RAG) architectures.</li><li>
Experience in DevOps/Platform Engineering roles with focus on developer tooling and productivity.</li><li>
Previous experience managing transitions from traditional to AI-augmented development practices.</li></ul>
The estimated base salary range for this position is $175,000 to $250,000, which is specific to New York and may change in the future. Millennium pays a total compensation package which includes a base salary, discretionary performance bonus, and a comprehensive benefits package. When finalizing an offer, we take into consideration an individual&rsquo;s experience level and the qualifications they bring to the role to formulate a competitive total compensation package.</div>

AI Platform Engineer

Delivery Hero

<div>Company Description 
Since launching in Kuwait in 2004, talabat, the leading on-demand food and Q-commerce app for everyday deliveries, has been offering convenience and reliability to its customers. talabat&rsquo;s local roots run deep, offering a real understanding of the needs of the communities we serve in eight countries across the region.
We harness innovative technology and knowledge to simplify everyday life for our customers, optimize operations for our restaurants and local shops, and provide our riders with reliable earning opportunities daily.
Here at talabat, we are building a high performance culture through engaged workforce and growing talent density. We're all about keeping it real and making a difference. Our 6,000+ strong talabaty are on an awesome mission to spread positive vibes. We are proud to be a multi great place to work award winner. 
Job Description 
Summary
 As the leading delivery platform in the region, we have a unique responsibility and opportunity to positively impact millions of customers, restaurant partners, and riders. To achieve our mission, we must scale and continuously evolve our machine learning capabilities, including cutting-edge Generative AI (genAI) initiatives. This demands robust, efficient, and scalable ML platforms that empower our teams to rapidly develop, deploy, and operate intelligent systems.
As an ML Platform Engineer, your mission is to design, build, and enhance the infrastructure and tooling that accelerates the development, deployment, and monitoring of traditional ML and genAI models at scale. You&rsquo;ll collaborate closely with data scientists, ML engineers, genAI specialists, and product teams to deliver seamless ML workflows&mdash;from experimentation to production serving&mdash;ensuring operational excellence across our ML and genAI systems. 
Qualifications 
Responsibilities<ul><li>
Design, build, and maintain scalable, reusable, and reliable ML platforms and tooling that support the entire ML lifecycle, including data ingestion, model training, evaluation, deployment, and monitoring for both traditional and generative AI models. 
</li><li>Develop standardized ML workflows and templates using MLflow and other platforms, enabling rapid experimentation and deployment cycles. 
</li><li>Implement robust CI/CD pipelines, Docker containerization, model registries, and experiment tracking to support reproducibility, scalability, and governance in ML and genAI. 
</li><li>Collaborate closely with genAI experts to integrate and optimize genAI technologies, including transformers, embeddings, vector databases (e.g., Pinecone, Redis, Weaviate), and real-time retrieval-augmented generation (RAG) systems. 
</li><li>Automate and streamline ML and genAI model training, inference, deployment, and versioning workflows, ensuring consistency, reliability, and adherence to industry best practices. 
</li><li>Ensure reliability, observability, and scalability of production ML and genAI workloads by implementing comprehensive monitoring, alerting, and continuous performance evaluation. 
</li><li>Integrate infrastructure components such as real-time model serving frameworks (e.g., TensorFlow Serving, NVIDIA Triton, Seldon), Kubernetes orchestration, and cloud solutions (AWS/GCP) for robust production environments. 
</li><li>Drive infrastructure optimization for generative AI use-cases, including efficient inference techniques (batching, caching, quantization), fine-tuning, prompt management, and model updates at scale. 
</li><li>Partner with data engineering, product, infrastructure, and genAI teams to align ML platform initiatives with broader company goals, infrastructure strategy, and innovation roadmap. 
</li><li>Contribute actively to internal documentation, onboarding, and training programs, promoting platform adoption and continuous improvement.</li></ul>
Requirements
Technical Experience<ul><li>
Strong software engineering background with experience in building distributed systems or platforms designed for machine learning and AI workloads. 
</li><li>Expert-level proficiency in Python and familiarity with ML frameworks (TensorFlow, PyTorch), infrastructure tooling (MLflow, Kubeflow, Ray), and popular APIs (Hugging Face, OpenAI, LangChain). 
</li><li>Experience implementing modern MLOps practices, including model lifecycle management, CI/CD, Docker, Kubernetes, model registries, and infrastructure-as-code tools (Terraform, Helm). 
</li><li>Demonstrated experience working with cloud infrastructure, ideally AWS or GCP, including Kubernetes clusters (GKE/EKS), serverless architectures, and managed ML services (e.g., Vertex AI, SageMaker). 
</li><li>Proven experience with generative AI technologies: transformers, embeddings, prompt engineering strategies, fine-tuning vs. prompt-tuning, vector databases, and retrieval-augmented generation (RAG) systems. 
</li><li>Experience designing and maintaining real-time inference pipelines, including integrations with feature stores, streaming data platforms (Kafka, Kinesis), and observability platforms. 
</li><li>Familiarity with SQL and data warehouse modeling; capable of managing complex data queries, joins, aggregations, and transformations. 
</li><li>Solid understanding of ML monitoring, including identifying model drift, decay, latency optimization, cost management, and scaling API-based genAI applications efficiently.</li></ul>
Qualifications<ul><li>
Bachelor&rsquo;s degree in Computer Science, Engineering, or a related field; advanced degree is a plus. 
</li><li>2+ years in a tech lead role, 5+ years of experience in ML platform engineering, ML infrastructure, generative AI, or closely related roles.</li><li>
Proven track record of successfully building and operating ML infrastructure at scale, ideally supporting generative AI use-cases and complex inference scenarios. 
</li><li>Strategic mindset with strong problem-solving skills and effective technical decision-making abilities. 
</li><li>Excellent communication and collaboration skills, comfortable working cross-functionally across diverse teams and stakeholders. 
</li><li>Strong sense of ownership, accountability, pragmatism, and proactive bias for action.</li></ul></div>

Staff Engineer - ML Platform

Appgain

APPGAIN is seeking an innovative and experienced AI Engineer to join our dynamic team. As an AI Engineer, you will be instrumental in designing, developing, and deploying cutting-edge artificial intelligence and machine learning solutions that address complex business challenges. You will work at the intersection of data science, software engineering, and operations to bring AI models from research to production.Responsibilities:<ul><li>Design, develop, and implement scalable AI models and algorithms using machine learning and deep learning techniques.</li><li>Build and manage robust AI development and production infrastructure, including data pipelines and MLOps workflows.</li><li>Collaborate with data scientists, product managers, and other stakeholders to translate business requirements into AI-driven solutions.</li><li>Integrate AI models into existing software systems and applications, ensuring seamless functionality and performance.</li><li>Conduct statistical analysis and interpret results to optimize AI models and guide decision-making.</li><li>Perform model testing, validation, and debugging to ensure accuracy, reliability, and efficiency.</li><li>Monitor and maintain deployed AI systems, identifying and resolving issues, and continuously optimizing performance.</li><li>Stay up-to-date with the latest advancements in AI research, technologies, and industry best practices.</li><li>Contribute to the adoption of AI best practices across teams and promote a data-driven culture.</li><li>Document machine learning processes, models, and solutions thoroughly.</li></ul>Qualifications:<ul><li>Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related quantitative field.</li><li>Proven experience as an AI Engineer, Machine Learning Engineer, or a similar role, with a strong portfolio of deployed AI solutions.</li><li>Proficiency in programming languages commonly used in AI, such as Python (with libraries like TensorFlow, PyTorch, Keras) or Java.</li><li>Solid understanding of machine learning algorithms (e.g., supervised, unsupervised, reinforcement learning) and deep learning neural networks.</li><li>Experience with big data technologies (e.g., Apache Spark, Hadoop, MongoDB) and data processing techniques.</li><li>Strong knowledge of SQL and experience with database modeling and management.</li><li>Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and their AI/ML services.</li><li>Excellent problem-solving, analytical, and critical thinking skills.</li><li>Strong communication and interpersonal skills, with the ability to explain complex technical concepts to non-technical stakeholders.</li><li>Ability to work effectively in an agile, fast-paced, and collaborative environment.</li><li>Experience with Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning.</li><li>Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Docker, Kubernetes).</li><li>Experience with prompt engineering for Large Language Models (LLMs).</li><li>Familiarity with software design patterns and principles.</li><li>Contributions to open-source AI projects or relevant publications.</li></ul>Job Type: full time on siteJob Type: Full-timeEducation:<ul><li>Bachelor's (Preferred)</li></ul>Experience:<ul><li>AI Engineer: 3 years (Preferred)</li><li>machine learning algorithms : 3 years (Preferred)</li><li>big data technologies : 3 years (Preferred)</li></ul>

AI Engineer

Search 10000+ Machine learning infrastructure engineer jobs near you this November. New roles added regularly across leading industries. Apply today.