Your AI career in the UAE starts here

Job title: Machine Learning Engineer 1Location: Dubai, UAEAbout noonWe’re building an ecosystem of digital products and services that power everyday life across the Middle East—fast, scalable, and deeply customer-centric. Our mission is to deliver to every door every day. We want to redefine what technology can do in this region, and we’re looking for a (add title) who can help us move even faster.noon’s mission: Every door, every day.What you'll do:Team noon has some of the fastest, smartest, and hardest-working people we've encountered. As a Machine Learning Engineer (MLE1), Ads, you will be a core individual contributor responsible for the hands-on design, development, and deployment of production-grade Machine Learning models across our Ads division. You will be responsible for delivering high-impact models and deployments that power critical functions like ranking, bidding, and click-fraud detection.This role is deeply technical; you will partner with Product and Engineering teams across noon Ads to turn complex data into competitive advantagesDeeply engage in the technical work: building, training, and deploying production-grade models across our domainsContribute to solving "hard" problems involving relevance, ranking, bidding optimization, and fraud detectionPartner with the broader engineering team to implement optimal deployment strategies for heavy ML workloadsAdhere to and contribute to the standardization of ML Ops and best practices within the teamWhat you'll need:Experience: hands-on experience or relevant internships in Machine Learning or data science is preferredFoundational Knowledge: Strong academic or practical grounding in core machine learning concepts, statistics, and data structuresCoding & Frameworks: Proficiency in Python and familiarity with standard ML libraries (e.g., PyTorch, TensorFlow, Scikit-learn) and SQLGrowth & Collaboration: Eagerness to learn from senior engineers, collaborate across product and engineering teams, and rapidly ramp up on MLOps best practicesTech Stack: Basic exposure to cloud platforms (like GCP) and data processing pipelines.

Join one of the world's leading multi-strategy investment platforms as a Quant Researcher focused on Machine Learning and AI. This role sits within a highly successful systematic investment team that is investing heavily in next-generation research capabilities and the application of cutting-edge machine learning techniques to financial markets.The team is seeking exceptional researchers who can bring expertise from frontier AI, deep learning, large-scale modelling, and modern machine learning research into a fast-paced environment where ideas are rapidly tested and deployed. This is an opportunity to work on some of the most challenging prediction problems in the world while having direct impact on investment performance.ResponsibilitiesDevelop and implement machine learning models to identify predictive signals across global marketsResearch and apply state-of-the-art deep learning techniques for alpha generationDesign and test neural network architectures for financial forecasting and pattern recognitionWork with large, complex, and alternative datasets to uncover unique sources of investment insightConduct rigorous research, experimentation, and statistical validation of trading signalsCollaborate closely with Portfolio Managers, Quant Researchers, and Data ScientistsBuild scalable research infrastructure and production-ready modelling pipelinesStay at the forefront of developments in AI, machine learning, and quantitative researchContribute to the development of systematic investment strategies using modern ML techniquesRequirementsPhD or Master's degree in Machine Learning, Computer Science, Mathematics, Statistics, Physics, Engineering, or a related quantitative disciplineStrong background in machine learning research and modellingExperience building and deploying deep learning models in real-world environmentsExpertise in areas such as neural networks, representation learning, transformers, foundation models, reinforcement learning, probabilistic modelling, or large-scale optimisationExcellent programming skills in Python and familiarity with modern ML frameworks such as PyTorch or TensorFlowStrong mathematical and statistical foundationsDemonstrated ability to conduct independent research and solve complex problemsExperience from leading AI labs, deep technology companies, research institutions, or high-performance machine learning environments is highly desirablePreferred BackgroundsWe are particularly interested in candidates from:Leading AI research labsFrontier foundation model teamsDeep learning and applied AI organisationsResearch-focused technology companiesQuantitative research groupsHigh-performance computing and data science environmentsAcademic researchers with strong publication records in machine learning or artificial intelligenceWhat You'll GetOpportunity to apply cutting-edge AI research to real-world investment decisionsAccess to significant computational, data, and research resourcesHighly collaborative environment alongside world-class investors and researchersCompetitive compensation package with substantial upside potentialDirect impact on investment outcomes and business growthRelocation support to Dubai where applicableExposure to one of the most intellectually challenging applications of machine learningThis position is ideal for researchers who are passionate about solving difficult prediction problems, building innovative machine learning systems, and applying frontier AI techniques in a highly competitive and rewarding environment. Desired Skills and ExperienceSeeking elite ML Researchers and PhDs to develop deep learning models for alpha generation within a highly capitalised, technology-driven investment team.

About the RoleGlobal Aerospace Logistics (GAL) is seeking a talented and driven AI Machine Learning Engineer to join our AI and Digital Transformation team. This role is responsible for designing, developing, deploying, and maintaining machine learning solutions that drive operational efficiency, automation, and data-driven decision making across the organization.You will work closely with data scientists, software engineers and business stakeholders to build scalable AI applications and bring machine learning models into production environments.Responsibilities:Design, build, train, and deploy machine learning models.Develop scalable data pipelines and feature engineering workflows.Productionize AI and machine learning solutions.Deploy models through APIs, microservices, and cloud platforms.Monitor, retrain, and optimize model performance.Implement MLOps practices including CI/CD, model versioning, and monitoring.Ensure data quality, security, and compliance requirements are met.Document AI systems, models, and technical workflows.Collaborate across multidisciplinary teams to deliver AI initiatives.Qualifications:Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related field.Strong programming skills in Python.Experience with Java, C++, or JavaScript is an advantage.Experience Required:3-8 years of experience in Machine Learning, Data Science, or Software Engineering.Minimum 2 years of hands-on experience deploying machine learning models into production.Experience in machine learning model development, feature engineering, data pipelines, and model deployment, with knowledge of MLOps tools such as MLflow, Kubeflow, or Airflow.Experience with Generative AI, LLMs, NLP, Computer Vision, or similar AI technologies is an advantage

We're looking for a Senior AI Engineer to join our team in United Arab Emirates in a hybrid working mode. As a Senior AI Engineer, you will play a critical role in developing an agentic platform tailored for the Oil & Gas sector. Your work will directly impact business outcomes by enabling advanced automation, intelligent decision-making, and operational efficiency for our clients in the industry. This is an opportunity to contribute to innovative solutions that drive digital transformation and add measurable value to client operations.ResponsibilitiesDesign and develop scalable AI-driven agentic platforms for Oil & Gas applications Build, optimize, and maintain robust machine learning pipelines and models Collaborate with data scientists, engineers, and business stakeholders to translate requirements into technical solutions Implement data quality, reliability, and performance improvements across large, complex datasets Integrate AI solutions with existing enterprise systems and cloud platforms Lead technical discussions and mentor junior engineers within the team Contribute to best practices, coding standards, and technical documentation Troubleshoot, optimize, and refactor existing AI pipelines for performance and scalability Ensure compliance with industry standards and client requirements Drive innovation by exploring new AI technologies and methodologies RequirementsBachelor's or Master's Degree in Computer Science, Engineering, Mathematics, or related fields, or relevant work experience Strong experience in AI engineering, with recent hands-on coding as a core part of your daily role Expertise in building high-performance, distributed machine learning pipelines Experience with cloud platforms (Azure, AWS, or GCP) Solid understanding of data engineering concepts and relational databases Experience with CI/CD, Git, and modern DevOps practices for AI solutions Strong problem-solving, communication, and client-facing collaboration skills Knowledge of agentic systems and their application in industrial settings Familiarity with Oil & Gas industry data and workflows Nice to haveExposure to reinforcement learning or advanced agentic architectures Experience with Databricks, PySpark, or similar big data technologies Understanding of operational technology (OT) integration in Oil & Gas Certification in cloud or AI technologies Experience with data science workflows and analytics platformsWe offerEnd of service gratuityPrivate healthcare and life insuranceEmployee assistance programWellness programAnnual air travel allowance for expatriatesRegular performance feedback and salary reviewsGlobal travel medical and accident insuranceReferral bonusesLearning and development opportunities including in-house training and coaching, professional certifications, and courses

We're looking for a Senior AI Engineer – Agentic AI to join our team in UAE out of either our Dubai or Abu Dhabi offices in a hybrid working mode.In this role, you will design and build scalable agentic AI platforms that integrate Large Language Models (LLMs), multi-agent orchestration and retrieval-augmented generation (RAG) patterns into production-ready enterprise solutions. You will focus on creating reusable platform components, orchestration engines and governance frameworks that allow complex AI workflows to operate securely and efficiently at scale.You will be responsible for developing advanced orchestration capabilities, implementing evaluation and observability tooling and embedding enterprise controls for compliance and safety. If you are passionate about innovating with AI in real-world applications and scaling intelligent systems, this role offers an opportunity to make a significant impact in production-grade AI engineering.ResponsibilitiesDesign, build and deploy Generative AI and Agentic AI solutions from prototype to production Implement multi-agent orchestration patterns using frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel or OpenAI Agents SDK Develop the orchestration backbone for advanced workflows including planning, checkpointing, retries, fallback handling and resumption of long-running processes Build and optimize RAG pipelines, including chunking strategies, embeddings, vector/hybrid search and retrieval evaluation with grounded responses and citations Develop memory and context management solutions, including short-term and long-term stores and compaction strategies Write robust Python APIs and services (e.g., FastAPI), incorporating async execution, background jobs and containerized deployments Integrate enterprise systems and tools using protocols such as MCP, A2A, OpenAPI, REST and gRPC, ensuring graceful degradation and retries Apply enterprise security and governance practices including RBAC, prompt safety checks, traceability and secrets management Implement evaluation pipelines and observability frameworks using tools such as Langfuse, Arize or OpenTelemetry Contribute to architectural design decisions, code reviews and engineering standards for platform development RequirementsBachelor’s or Master’s degree in Computer Science, Engineering or related field (PhD is a plus) Practical experience delivering Generative AI or Agentic AI systems into production environments Expertise in Python engineering for APIs, microservices, testing and CI/CD workflows Strong working knowledge of LLM capabilities, including prompt design, structured outputs, tool calling and retrieval strategies Hands-on experience with agent orchestration frameworks (LangGraph, AutoGen, CrewAI or Semantic Kernel) Proven experience with RAG implementations, embeddings and vector database integrations Familiarity with stateful or long-running systems, including checkpointing and resumable workflows Cloud deployment experience (Azure preferred), using services such as Azure OpenAI, AI Foundry or AI Search, with Docker and Kubernetes Understanding of schema validation frameworks (e.g., JSON Schema, Pydantic) and MLOps tools such as MLflow or Airflow Strong communication ability to explain trade-offs around cost, latency and accuracy to technical and non-technical audiences Nice to haveExperience using Azure AI Foundry or Microsoft Agent Framework Knowledge of MCP and A2A protocols for agent and tool interoperability Hands-on work with vector databases like Pinecone, Weaviate, Qdrant or pgvector Familiarity with distributed systems, workflow engines (Temporal, Airflow or Dagster) and event-driven architectures Experience with open-source LLMs or Small Language Models for custom deployments Knowledge of AI safety and governance: guardrails, output filtering and red-teaming practices Background in fine-tuning or adapting foundation models (e.g., LoRA, distillation) for domain-specific tasksWe offerEnd of service gratuityPrivate healthcare and life insuranceEmployee assistance programWellness programAnnual air travel allowance for expatriatesRegular performance feedback and salary reviewsGlobal travel medical and accident insuranceReferral bonusesLearning and development opportunities including in-house training and coaching, professional certifications, and courses

ML engineerLocation: DubaiDuration: 12 monthsVisa : Work Permit, Dependent, Tourist Visa. Need Someone in Dubai only. Position SummaryAs an ML Engineer (MLOps), you will take machine-learning models and AI pipelines from proof-of-concept through to scalable, reliable production deployment. You will own deployment, monitoring, and optimization across both edge and cloud environments.Responsibilities• Deployment: Deploy ML models and AI pipelines from PoC / development to production, ensuring they scale efficiently and maintain high performance through seamless CI/CD integration and orchestration.• Monitoring & Maintenance: Implement monitoring and maintenance strategies for deployed models to ensure ongoing accuracy and reliability.• Model Optimisation & Pruning: Optimise models for inference speed and resource efficiency using techniques such as quantisation, pruning, and knowledge distillation for edge and cloud deployment.• Data Preprocessing: Perform data collection, cleaning, and feature engineering to prepare datasets for training.• Model Training & Tuning: Implement continuous / semi-continuous training and evaluation workflows to maintain accuracy over time, and fine-tune models for optimal performance.• Collaboration: Work with data scientists, software engineers, DevOps, and product managers to understand requirements and deliver ML solutions.• Documentation: Maintain clear, organised documentation of code, models, and processes.Qualifications• Bachelors or Masters degree in Computer Science, Machine Learning, Data Science, AI, or a related field.• 5 – 9 years of relevant experience.• Proficiency in Python and libraries such as PyTorch, NumPy, Pandas, and Scikit-learn.• Knowledge of model deployment, containerisation, and orchestration (Docker, Kubernetes).• Knowledge of SQL and NoSQL databases.• Familiarity with one or more cloud platforms (AWS, GCP, or Azure).• Familiarity with MLOps tools such as MLflow, ClearML, Azure ML, or AWS SageMaker.• Strong understanding of deep learning, reinforcement learning, and other ML techniques.Preferred Qualifications• Experience deploying computer-vision models to edge devices or low-resource environments.• Familiarity with infrastructure-as-code tools and observability platforms.• Contributions to open-source computer-vision projects or relevant publications.Core Technical Skills• Languages: Python.• Frameworks & Libraries: PyTorch, TensorFlow, OpenCV, Scikit-learn, Pandas, NumPy, FastAPI.• Serving ; Deployment: Docker, Kubernetes, GitLab CI (CI/CD).• Databases: PostgreSQL, MySQL, MongoDB, Elasticsearch, Neo4j.• Deep-Learning Architectures: CNN, LSTM, GAN, Transformers, LLM.• MLOps & Distributed Computing: MLflow, Kubeflow, Ray, ClearML.• Message Brokers & GPU: RabbitMQ, Kafka; CUDA, RAPIDS, Numba.• Cloud Platforms: AWS, Azure, GCP.