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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 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

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

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

careem

<div>
About the team: 
The AI Acceleration Office builds the intelligence layer that powers Careem s ecosystem, and we are looking for a Senior Software Engineer to drive architecture and development of it. Our mission is to drive competitive value through an Agentic AI Platform, a system that can design, orchestrate, and manage autonomous AI agents across customer and operational journeys. 
 About the role: 
As a Senior Software Engineer, you ll lead the architecture and development of the AI Agent Creation and Management Platform, an intelligent layer that enables scalable agent creation, orchestration, lifecycle management, and performance monitoring. You ll work acrossbackend systems, AI infrastructure, and data orchestration, designing abstractions that connect LLMs, context, memory, and state into real-time adaptive agents. This is a hands-on leadership role, you ll design systems, ship code, mentor engineers, and influence Careem s long-term AI platform strategy./p&gt;

What you ll do: 
<ul style="">
<li>
Architect and implement the foundational services for Careem s Agentic AI Platform, covering agent creation APIs, orchestration pipelines, state machines, and monitoring layers./p&gt;

</li>
<li>
Design abstractions for multi-agent collaboration, memory persistence, and reasoning cycles integrated./p&gt;

</li>
<li>
Build scalable orchestration frameworks (LangGraph, or custom) that coordinate model calls, context injection, and multi-turn workflows across internal and external agents./p&gt;

</li>
<li>
Develop observability and governance systems for AI agents: tracing, metrics, auditing, and self-healing./p&gt;

</li>
<li>
Integrate LLM-based and retrieval-augmented components into backend services with strict latency, reliability, and compliance requirements./p&gt;

</li>
<li>
Work with data scientists and ML engineers to operationalize models (embedding stores, vector databases, evaluation pipelines, model versioning)./p&gt;

</li>
<li>
Establish standards for agent safety, lifecycle management, and deployment reliability across Careem s product ecosystem./p&gt;

</li>
<li>
Mentor engineers and shape platform-wide engineering practices around AI integration and distributed systems design./p&gt;

</li>
</ul>
What you ll need: 
<ul style="">
<li style="">
6+ years of backend or distributed systems engineering experience, with 2 3 years in AI/ML infrastructure or LLM-based systems./p&gt;

</li>
<li style="">
Deep hands-on experience in Python and Go, with production experience in building scalable, event-driven architectures./p&gt;

</li>
<li style="">
Expertise in orchestration and distributed compute (Kubernetes)./p&gt;

</li>
<li style="">
Experience in building end-to-end event-drive architecture and data pipelines (Kafka, Airflow and Flink)/p&gt;

</li>
<li style="">
Familiarity with agentic frameworks (LangGraph, LangChain, AutoGen) and design of multi-agent or RAG systems./p&gt;

</li>
<li style="">
Experience in integrating LLMs and vector stores (FAISS, Pinecone, Weaviate, Milvus) into backend applications./p&gt;

</li>
<li style="">
Solid understanding of model and agent observability, monitoring, and continuous evaluation./p&gt;

</li>
<li style="">
Strong architectural skills, capable of designing systems that balance scalability, cost, and experimentation velocity./p&gt;

</li>
<li style="">
Experience working with data streaming, metrics, and real-time feedback loops./p&gt;

</li>
<li style="">
Growth mindset and bias for ownership, you thrive in high-autonomy, fast-paced, and purpose-driven environments./p&gt;

</li>
</ul>
</div>

Senior Software Engineer II - AI Platform

EMIRATES GLOBAL ALUMINIUM (EGA)

<ul><li>Big Data Infrastructure: Design build, and maintain high-performance, cloud-based, fault-tolerant, scalable distributed Data infrastructure that supports the company’s data-intensive applications Real time/Batch/LLM’s. This includes developing strategies for data storage TBs, processing, and analysis, and implementing high-performance, scalable data pipelines for ML models and data products, supporting up to 50-60 use cases a year and thousands of IoT devices.</li><li>Create infrastructure as Code, perform configuration and set up managed data services. Build and deploy a data science playground for research and prototyping for the professional and citizen data science program being rolled out and supporting 15-20 citizen data scientists/ambassadors.</li><li>Machine Learning Operations MLOps Develop and manage the ML operational process, working closely with the data science team to implement ML models into production, including edge. This includes streamlining the ML lifecycle, from model development and testing to deployment and implementing the monitoring and alerting strategy.</li><li>Automation and Scalability: Implement automation tools and frameworks to manage system updates/changes. Ensure that all systems and infrastructures can scale effectively with the increase in IoT sensors and devices.</li><li>Continuous Integration and Deployment CI CD: Oversee continuous integration and continuous deployment practices for the data and ML pipeline, ensuring that software can be reliably released at any time.</li><li>System Monitoring and Reliability: Monitor system performance and reliability to ensure high levels of performance, availability, and security This includes identifying and fixing potential and existing system issues. Collaboration and Communication Strong collaboration with Data Architect/Engineer, data scientists for the implementation and testing of new data services to provide an elastic data infrastructure.</li><li>Security: Oversee and ensure that all Big Data and ML Ops platforms comply with the companies security standards and policies.</li><li>Mentorship and Leadership: Act as a mentor to junior data members, providing guidance and support in their professional development. Promote a culture of performance, collaboration, and continuous learning within the data team.</li><li>Innovation and Continuous Improvement: Stay up to date with industry trends and new technologies. Continuously explore innovative solutions and enhancements to the existing data architecture to improve its scalability, reliability, and efficiency.</li><li>Problem Solving: Anticipate and resolve technical issues before they become roadblocks, maintaining the continuity of data flow and ensuring the highest levels of data quality and integrity.</li></ul>AUTHORITY/DECISION MAKING:<ul><li>Infrastructure Design: Decide on the most effective design and implementation of the companys Big Data infrastructure.</li><li>ML Ops Process: Make key decisions on the ML operational process, ensuring that ML models can be effectively integrated into production.</li><li>Automation Tools: Choose the most appropriate automation tools and frameworks for the company&#x27;s needs.</li><li>CI/CD Practices: Determine the best practices for continuous integration and deployment in the context of the company&#x27;s operations.</li><li>System Monitoring: Make decisions on system monitoring strategies, including the selection of tools and responses to system performance metrics.</li><li>Security Policies: Have a say in the implementation of security policies as they pertain to the Big Data and ML Ops platforms.</li><li>Budget and Costing: Taking ownership of managing data platform costs and relevant data services.</li></ul>

Lead Data Ops Engineer

Nybl

About the role:We are seeking a highly experienced Subject Matter Expert (SME) in Electrical Submersible Pump (ESP) systems to join our team and guide the development of Lift.ai, an advanced analytical and machine learning platform for production optimization. This role is designed for a senior professional who combines deep ESP domain knowledge with the ability to collaborate on the integration of cutting-edge ML solutions that deliver unprecedented value to operators.Responsibilities:<ul><li>Act as the technical authority for ESP design, operations, and optimization within n.lift</li><li>Define and validate core ESP performance calculations, physics-based workflows, and diagnostic methodologies.</li><li>Work with data scientists to embed machine learning models into engineering workflows, ensuring results are physically sound, explainable, and actionable.</li><li>Review and validate hybrid (physics + ML) algorithms for optimization, prediction, and anomaly detection.</li><li>Provide domain expertise to ensure ML outputs align with real-world ESP performance, failure modes, and operational constraints.</li><li>Collaborate with software engineers to translate ESP physics, correlations, and field practices into scalable algorithms and user-facing tools.</li><li>Mentor junior engineers and contribute to knowledge transfer across the team.</li><li>Support product strategy by identifying high-value use cases for analytics + ML integration in artificial lift management.</li><li>Represent Lift.ai as an industry thought leader in the application of advanced analytics and ML to ESP systems.</li></ul>Qualifications:<ul><li>10+ years of direct experience with ESP systems (design, operations, optimization, or troubleshooting).</li><li>Expertise in ESP diagnostics (power, current, voltage, vibration, performance degradation, failure mechanisms).</li><li>Solid understanding of petroleum production systems, artificial lift methods, and multiphase flow.</li><li>Experience collaborating with software/ML teams on applied AI solutions (not required to build ML models, but able to guide and validate them technically).</li><li>Strong analytical mindset with the ability to balance physics-based engineering and data-driven insights.</li><li>Excellent communication skills for cross-functional collaboration and client engagement.</li><li>Advanced degree (preferred) or equivalent experience in Petroleum, Electrical, or Mechanical Engineering.</li></ul>This is a strategic role at the intersection of engineering expertise and machine learning innovation. As SME, you will ensure that Lift.ai becomes the industry s most trusted AI-driven ESP optimization and system health platform, combining the best of engineering science with the power of data.

Subject Matter Expert

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