Your AI career in the UAE starts here

AI Engineer / Forward Deployed Engineer - AI & Agentic SystemsAbout the RoleDomain Rewiring means redesigning the operational domain itself — workflows, decision points, data flows and human roles — so that agentic AI becomes embedded into how work actually gets done.The Forward Deployed Engineering team works directly inside government entities to identify, build and deploy these systems, ensuring the underlying AI platform is shaped by real operational requirements rather than developed in isolation.Why JoinWork on complex applied AI problems. Re-engineer live government workflows and deploy agentic AI where operational decisions are actually made.Own outcomes, not just engineering. Define the users, workflow, success metrics and scope, then take the solution through to production and adoption.Small, senior teams. Work in compact squads of experienced engineers with significant autonomy and direct access to senior government stakeholders.Build the practice. Help establish the playbook for evaluation, AI-native software development and forward deployment, before helping grow the team around it.UAE-based opportunity. Relocation assistance is available for successful candidates and their families.The RoleWe are hiring a AI Engineer / Forward Deployed Engineer to join a small team of senior engineers responsible for taking complex government domains from discovery through to production AI systems.This is an individual contributor role first.You will personally own some of the organisation’s most challenging domain rewirings, working directly with users and senior stakeholders to understand how an operation works today and redesign how it should operate in an AI-native environment.There is no traditional business analyst, product manager or architect sitting between you and the problem. You define the users, workflow, scope, success metric and value case — and then build.You will own domain rewiring, adoption and realised business value, while partnering with specialists across AI engineering, product engineering, platform architecture and evaluation.As the programme grows, you may also lead a rewiring pod, hire engineers or own part of the domain portfolio. However, leadership sits alongside hands-on delivery rather than replacing it.What You OwnEmbed and define. Work directly inside government entities, understand how operations actually run and define the requirements yourself. Establish the metric, capture the baseline and agree it with the business owner before building.Design and ship. Architect agentic workflows spanning orchestration, retrieval, tools, agents, evaluation and human-in-the-loop processes — then write the production code.Navigate enterprise constraints. Take deployments through security, privacy, tenant isolation, governance and information-security reviews, creating reusable patterns for future deployments.Own adoption. Redesign the human process alongside the software. Success means changing how decisions are made, reducing cycle times, lowering costs or improving measurable outcomes.Prove value. Instrument products so their impact can be measured against an agreed baseline. Recommend scaling, redesigning or stopping initiatives based on evidence.Make the next deployment faster. Convert lessons from individual rewirings into reusable platform capabilities, engineering patterns and playbooks.First Three MonthsIndicative outcomes include:Delivering one lighthouse domain rewiring end-to-end within a major government entity, reaching production and demonstrating measurable improvement.Establishing an agreed value case and baseline with the relevant business owner and delivering the first value report.Contributing reusable capabilities to the underlying sovereign AI platform based on requirements discovered through real-world deployment.Who You AreStrong production engineer.You have personally shipped software end-to-end. Years of experience matter less than what you have successfully put into production.Field-hardened.You thrive in ambiguous, embedded and stakeholder-facing environments where the problem has not yet been clearly defined.Hands-on with agentic AI.You have practical depth across LLM and agent systems, including orchestration, retrieval, evaluation, agent design and prompt engineering. AI coding tools are part of your normal development workflow.Product owner by instinct.You are comfortable interviewing users, defining requirements, prioritising problems and cutting scope without waiting for a Product Manager.Business literate.You can discuss cost, cycle time, productivity, headcount and ROI with senior executives without hiding behind technical terminology.Enterprise literate.You understand identity, networking, data governance, security and approval processes required to move software into production within complex organisations.Value disciplined.You are willing to stop, redesign or descope your own work when the evidence shows it will not create sufficient value.Strongly PreferredForward Deployed Engineering, Solutions Engineering or technical delivery experience.Experience building or leading an FDE capability.Delivery experience within government, financial services or another regulated/high-assurance environment.Founder, founding engineer or zero-to-one product experience.Hands-on cloud and platform engineering experience, particularly Azure.Arabic and English capability, or experience operating effectively within bilingual environments.Evidence We Will Ask ForWe will want to understand:Two products you personally built that reached real users in production, including your contribution, scale and measurable outcome.For at least one product, the baseline before deployment, measured result afterwards, how the impact was measured and who validated it.One product you stopped, redesigned or significantly descoped because the expected value did not materialise, including the evidence behind that decision.Projected benefits in a business case are not considered realised impact.How Performance Is MeasuredPerformance is based on three primary outcomes:Speed: Time from problem definition to a rewired workflow being used in production.Value: Measurable impact against an agreed baseline, validated by the relevant business owner.Judgement: Rewirings that successfully pass the value checkpoint — and initiatives correctly stopped early when the evidence does not support further investment.Performance is not measured by lines of code, number of pilots or size of team.What This Role Is NotNot a management role disguised as engineering.You may lead engineers or a portfolio, but you remain personally responsible for delivering rewirings.Not advisory.Recommendations that end with a presentation or strategy document do not count. You build and ship.Not a pilot factory.A portfolio of proofs of concept that never reaches production or creates measurable value is not considered successful.

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.

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.

About the roleAs a Forward Deployed Engineer (FDE) at Albatha Group, you embed with our internal customers to turn AI from a promising demo into a production system that moves their business. You will own the full arc: understand the customer's problem, design and build LLM-powered solutions, deploy them into messy real-world environments, and feed what you learn back into our product.This role suits engineers who like being close to the problem, can write production-grade code, and are equally comfortable in a whiteboard session with a C level and a debugging session with a data team.What you'll doEmbed with customer teams to understand their workflows, data, and constraints, and identify where AI can deliver measurable value.Scope and prioritize use cases with technical and business stakeholders, turning ambiguous problems into clear delivery plans.Design and build production AI solutions: LLM-based agents, retrieval-augmented generation (RAG), tool/function calling, document processing, and workflow automation.Integrate with customer systems such as data warehouses, CRMs, ticketing tools, internal APIs, and legacy software.Build evaluation harnesses and monitoring so quality, latency, cost, and safety are measured, not guessed.Deploy and operate solutions in customer environments, including cloud, VPC, and on-prem setups with security and compliance requirements.Iterate with end users after launch, tuning prompts, retrieval, models, and UX based on real usage.Turn repeated customer needs into reusable components, and bring product and research teams concrete feedback from the field.Present results, trade-offs, and roadmaps to executives and engineers alike.What you'll bring4+ years of software engineering experience, with strong Python and working fluency in TypeScript or a similar language.Hands-on experience shipping LLM applications to production: prompt design, RAG, agents, tool use, structured outputs, and fine-tuning where it fits.Practical knowledge of evaluating AI systems, including building test sets, using LLM-as-judge methods, and tracking regressions.Experience with vector search, embeddings, and data pipelines, plus solid SQL and API integration skills.Comfort with cloud infrastructure (AWS, GCP, or Azure), containers, and CI/CD.Clear communication with both technical and non-technical audiences, including the ability to run workshops and explain trade-offs plainly.A bias for ownership: you take a problem from vague request to deployed, working solution.Willingness to travel to customer sites as needed (see logistics below).Nice to havePrior experience in a forward deployed, solutions engineering, consulting, or startup founding role.Experience with agent frameworks, Model Context Protocol (MCP), or multi-agent orchestration.Background in a regulated industry such as finance, healthcare, or government, including security reviews and data governance.Familiarity with model serving, inference optimization, or open-weight model deployment.Experience with front-end work to build quick internal tools and prototypes for customers.Contributions to open-source AI tooling or published technical writing.

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

About The Job You Are ConsideringCapgemini’s Insights & Data practice helps leading organizations harness data and Artificial Intelligence to drive innovation, efficiency, and business transformation. As organizations accelerate adoption of Generative AI and Agentic AI technologies, we are expanding our capability to deliver intelligent, scalable, and secure AI solutions that create measurable business value.We are looking for a Senior Agentic AI Solution Specialist to join our growing AI team. In this client-facing role, you will work with business and technology stakeholders to design, develop, and implement advanced AI solutions. You will help clients translate business challenges into scalable AI architectures, leveraging Agentic AI, Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and modern cloud technologies.The places that you work from day to day will vary according to your role, your needs, and those of the business; it will be a blend of Company offices, client sites, and your home, noting that you will be unable to work at home 100% of the time.Your RoleDesign and develop enterprise AI solutions, with a particular focus on Agentic AI, Generative AI, and RAG architectures.Work with clients to understand business requirements and translate them into scalable technical solution designs.Build, integrate, and optimize AI applications using Python and leading AI development frameworks.Implement and enhance AI solutions through prompt engineering, embeddings, orchestration frameworks, and security controls.Develop solutions on cloud platforms including Azure, AWS, and Google Cloud Platform, utilizing modern AI and data services. Collaborate with architects, data scientists, software engineers, and business stakeholders throughout the project lifecycle.Provide technical leadership on AI solution architecture, best practices, and technical delivery. Keep pace with advancements in Large Language Models, Agentic AI, cloud technologies, and emerging AI frameworks, incorporating relevant innovations into client solutions.Support business development activities, client workshops, proof-of-concepts, and solution demonstrations where required.Ensure AI solutions align with security, governance, Responsible AI, and enterprise architecture standards.Your Skills And ExperienceDegree in Computer Science, Information Systems, Engineering, Mathematics, Data Science, or a related STEM discipline, or equivalent professional experience.Strong experience developing AI solutions using Python and modern AI frameworks such as LangChain or similar technologies.Deep understanding of LLM architectures, Generative AI, Agentic AI concepts, RAG frameworks, embeddings, and prompt engineering.Experience designing and deploying solutions on Azure, AWS, or GCP cloud platforms. Experience working in a technical client-facing role, preferably delivering AI or data-driven transformation programmes.Strong understanding of AI solution architecture, systems integration, and enterprise application development.Ability to communicate complex technical concepts effectively to both technical and non-technical audiences.Experience working in consulting or professional services environments is advantageous. Knowledge of security, privacy, governance, and Responsible AI practices.Strong stakeholder management, problem-solving, and collaboration skills.Why you should consider CapgeminiGrowing clients’ businesses while building a more sustainable, more inclusive future is a tough ask. When you join Capgemini, you’ll join a thriving company and become part of a collective of free-thinkers, entrepreneurs and industry experts. We find new ways technology can help us reimagine what’s possible. It’s why, together, we seek out opportunities that will transform the world’s leading businesses, and it’s how you’ll gain the experiences and connections you need to shape your future.By learning from each other every day, sharing knowledge, and always pushing yourself to do better, you’ll build the skills you want. You’ll use your skills to help our clients leverage technology to innovate and grow their business. So, it might not always be easy, but making the world a better place rarely is.About CapgeminiCapgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organisations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of over 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2025 global revenues of €22.5 billion.Make it real | www.capgemini.com