AI / ML Engineer I Dubai | Financial Services| Orient Insurance PJSC

Job Requisition ID: 182415 About Al-Futtaim Established in the 1930s, Al-Futtaim is one of the most diversified and progressive privately held regional businesses, headquartered in Dubai. Operating across more than 20 countries, the Group spans automotive, financial services, real estate, retail and health. With more than 40,000 employees, Al-Futtaim represents over 200 of the world’s most recognised brands, including Toyota, Lexus, IKEA, ACE and Marks & Spencer. Driven by its purpose to enrich lives and elevate communities, Al-Futtaim focuses on digital innovation, AI, sustainable growth and strategic partnerships, creating long-term value for customers, communities and partners while building a responsible legacy for future generations. About the Role We are looking for an experienced AI / ML Engineer to design, build, and operationalize Artificial Intelligence and Machine learning systems that deliver measurable business value. This role takes models from experimentation to robust, scalable production services. You will partner with domain experts and product managers to translate business problems into well-engineered AI solutions, spanning classical ML, LLMs, RAG and emerging agentic AI workflows. The position suits an engineer who is equally comfortable training and fine-tuning models, building reliable data and inference pipelines, and owning the MLOps lifecycle that keeps those systems performant, governed, and cost-efficient in production. This is a high-impact role for someone who wants to shape how AI is deployed responsibly and at scale across the enterprise. Key Responsibilities - Develop and integrate generative AI and LLM-based solutions, including prompt engineering, RAG architectures, embeddings/vector search, fine-tuning, and evaluation of model outputs. - Design and implement agentic AI workflows — orchestrator/sub-agent topologies, tool/function calling, and integrations via protocols such as the Model Context Protocol (MCP) with appropriate guardrails and human-in-the-loop controls. - Design, develop, train, and deploy machine learning and deep learning models for production use cases (e.g., prediction, classification, NLP, recommendation, computer vision). - Build and maintain end-to-end ML pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, and monitoring. - Operationalize models through robust MLOps practices: CI/CD for ML, model versioning, automated retraining, A/B testing, and performance/drift monitoring. - Collaborate with data engineers to ensure reliable, well-governed data flows and feature stores supporting model performance. - Optimize models and inference infrastructure for latency, throughput, scalability, and cost. - Implement observability, logging, and alerting for deployed AI systems; diagnose and resolve issues including data drift, model degradation, and quality regressions. - Embed responsible AI principles — fairness, explainability, security, privacy, and compliance — into the design and deployment lifecycle. - Contribute to architecture decisions, code reviews, technical documentation, and engineering best practices. - Translate ambiguous business requirements into clear technical solutions and communicate outcomes to both technical and non-technical stakeholders. Required Skills & Experience Technical - 3–7+ years of hands-on experience building and deploying AI/ML systems in production - Strong programming skills in Python, with solid software engineering fundamentals (testing, version control, modular design, code review). - Practical experience with LLMs and generative AI — including prompt design, RAG, embeddings, vector databases, and model evaluation. - Proven experience with ML frameworks such as scikit-learn, PyTorch, and/or TensorFlow. - Solid grounding in core ML/DL concepts: supervised/unsupervised learning, model evaluation, feature engineering, NLP, and neural network architectures. - Experience deploying models as production services (REST/gRPC APIs, containerization, batch and real-time inference). - Strong data skills: SQL, data manipulation (e.g., pandas/Spark), and working with large, real-world datasets. - Experience with at least one major cloud platform (Azure, AWS, or GCP) and its ML services. - Familiarity with MLOps tooling and the practical realities of model monitoring, retraining, and lifecycle management. Functional - Ability to translate business problems into well-scoped, measurable AI solutions. - Strong analytical and problem-solving skills with a pragmatic, outcome-focused mindset. - Clear written and verbal communication; able to explain technical trade-offs to diverse stakeholders. - Comfortable working in cross-functional, agile teams and collaborating across data, product, and engineering. - Sound judgment on model risk, data governance, and responsible AI considerations. Preferred Qualifications - Exper