Machine Learning Jobs in Dubai

Quantitative Researcher — Systematic StrategiesUAE | Full-timeHiring for a world-leading institutional investor building out a quant research division and dedicated AI/ML research lab. Significant compute resources, long time horizons, and serious capital behind systematic strategies.The Role Develop alpha signals and systematic strategies using ML, deep learning, and statistical techniques across equities, rates, FX, and multi-asset. Take research from ideation to production. Collaborate with PMs, engineers, and data teams.YouPhD or equivalent in ML, statistics, maths, CS, or quant financeExperience building trading signals in a live environmentPython required, C++ a plusWhy HereMassive investment in quant and AI capabilitiesScale and resources few firms can matchLong-horizon research culture - not chasing quarterly P&LUAE - tax-free, high quality of lifeAll applications confidential

We are looking for a hands‑on AI/ML Engineer to own and execute MLOps, evaluation, and deployment practices for a production AI platform built on LLMs, agentic workflows, vision, and voice AI.This role is strongly execution‑focused. You will work across the entire AI lifecycle—from evaluation and observability to RLHF, deployment in constrained environments, and production readiness sign‑off—while collaborating with internal teams and directing external vendors.Key ResponsibilitiesMLOps & Deployment OwnershipDefine and oversee MLOps practices including:Agent and model versioningEvaluation trackingDeployment gating and promotion workflowsRollback and recovery proceduresCollaborate with internal stakeholders and external delivery teams to ensure reliable production deployments.Evaluation, Monitoring & ObservabilityOwn the evaluation framework for:LLM‑based agentsRAG pipelinesVision Language Models (VLMs)Voice AI models (OpenAI Whisper, Chatterbox, Vibe Voice, or equivalent)Define and maintain:Offline evaluation methodologiesOnline monitoring and regression detection thresholdsHuman‑in‑the‑loop review processesSet up and manage AI observability tooling (e.g., Langfuse or equivalent) across all environments.Performance Reporting & InsightsBuild and maintain product performance reporting, covering:Model accuracy and agent effectivenessLatency and cost‑per‑interactionBias, quality trends, and stability across marketsProvide clear technical insights to non‑technical stakeholders.RLHF & Continuous ImprovementDesign and oversee RLHF (Reinforcement Learning from Human Feedback) pipelines:Data collection and feedback ingestionAnnotation guidelines and reward criteriaFeedback loops for continuous improvementDirect implementation by external teams and monitor quality improvements over time.Agent Memory SystemsOwn the design and validation of agent memory architectures, including:Short‑term context windowsLong‑term retrievalEpisodic memory across sessionsMemory lifecycle policies (retention, expiry, cost control)Define test criteria to ensure consistency across deployment environments.Model Benchmarking & OptimizationEvaluate and benchmark VLMs and voice models under constrained infrastructure.Recommend optimization strategies:QuantizationDistillationRuntime and model selection per jurisdictionValidate production readiness in on‑prem or sovereign environments.Production Readiness & RolloutsOversee production deployments executed by vendor teams.Run final validation checks and sign off on production readiness.Document deployment patterns, baselines, and environment‑specific configurations to accelerate future market rollouts.Privacy & Data ResidencyEvaluate and recommend privacy‑preserving deployment patterns, including:On‑device inferenceData isolationLocal or sovereign model hostingEnsure compliance with jurisdictional data residency requirements.Technical Requirements3–5 years of experience in applied AI, LLMOps, MLOps, or similar technical AI roles.Strong Python expertise:Type hints, async programming, FastAPICode reviews, evaluation scripts, prototyping pipelinesExperience with LLM application patterns:RAG pipelinesPrompt engineeringMulti‑agent orchestrationSolid background in supervised ML (scikit‑learn, XGBoost, LightGBM, or equivalent).Strong understanding of MLOps fundamentals:Model versioningExperiment trackingCI/CD deployment pipelinesMonitoring and rollback strategiesHands‑on experience with:RLHF or human‑feedback‑driven improvement loopsLLM/VLM/voice AI evaluation frameworksAgent memory architecturesWorking knowledge of:Vision Language Models (VLMs)Voice AI systems across latency, language, and hosting constraintsUnderstanding of model optimisation techniques (quantization, distillation, ONNX).Experience using AI observability tools (Langfuse, LangSmith, or equivalent).Comfortable directing or overseeing external/vendor engineering teams.Ability to work independently in ambiguous and non‑standard infrastructure environments.Good to HaveExperience with sovereign cloud or government‑regulated infrastructure.Familiarity with agentic AI frameworks (LangChain/LangGraph, CrewAI, PydanticAI).Exposure to federated learning or privacy‑preserving inference.Background in healthcare, insurance, or regulated domains.Experience building performance dashboards for non‑technical audiences.Immediate Joiner - Required