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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 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

5+ years of enterprise software development, including 2+ years of hands-on AI/GenAI experienceStrong Python development and production AI/LLM engineering experienceProven experience building Generative AI solutions for financial services, including trading, investment operations, research, compliance or productivity use casesDeep hands-on LLM fine-tuning and model adaptation experience: LoRA / QLoRAPEFTSupervised fine-tuningInstruction tuningPreference tuningKnowledge distillationExperience deploying fine-tuned/custom models into production, including containerized inference, GPU infrastructure, model registries and model-serving frameworksStrong model evaluation and benchmarking skills — comparing base, fine-tuned, distilled and RAG approaches using quality, latency, throughput and cost metricsPractical experience with open-weight models, such as Llama, Mistral/Mixtral, Qwen, Gemma, Phi or similarExperience with LangChain, LangGraph and/or Semantic Kernel for GenAI workflows and agentic applicationsStrong Microsoft Azure experience, particularly Azure OpenAI, Azure Machine Learning and Azure Cognitive Search; AWS exposure is a plusStrong RAG, semantic retrieval and conversational memory experienceExperience developing autonomous / agentic AI systemsExperience with Docker, Kubernetes, CI/CD and observability for enterprise AI deploymentAbility to work with quants, traders and business stakeholders and translate business requirements into AI/model solutionsStrong problem-solving, communication, stakeholder management and Agile delivery skillsBachelor's/Master's degree in Computer Science or equivalent experienceThis role has a much stronger model-engineering focus, particularly around fine-tuning, open-source models, evaluation/benchmarking and production deployment of customized models.

Data scientistInternet City, DubaiDuration:12 monthsVisa : Work Permit, Tourist Visa, Dependent VisaNeed Someone in Dubai only.Responsibilities• Develop and fine-tune computer-vision models for object detection, tracking, and segmentation using PyTorch / TensorFlow and OpenCV.• Work hands-on with modern architectures including YOLO, RF-DETR, and Vision-Language Models (VLMs), fine-tuning them for target use cases.• Curate, preprocess, and augment image and video datasets, maintaining a tight feedback loop with the annotation team.• Train, tune, and evaluate models against rigorous metrics (mAP, precision, recall) and iterate to meet accuracy targets.• Track experiments and results using tools such as MLflow or Weights & Biases.• Apply model-optimisation techniques (TensorRT, ONNX) with real-world deployment constraints in mind.Qualifications• Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or a related field.• 3 – 7 years of applied machine-learning experience, with a focus on computer vision.• Strong Python skills and hands-on experience with PyTorch or TensorFlow and OpenCV.• Demonstrated experience with detection, tracking, and segmentation models (e.g. YOLO, RF-DETR).• Experience fine-tuning Vision-Language Models is an advantage.• Familiarity with experiment-tracking and model-optimisation