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

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 • Experience fine-tuning or adapting foundation models • Hands-on work with agentic frameworks (e.g., LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent) and multi-agent orchestration. • Experience building RAG pipelines at scale, including chunking strategy, retrieval tuning, and grounding/evaluation. • Familiarity with distributed training, GPU optimization, and model serving at scale. • Experience with streaming/real-time data and event-driven architectures. • Exposure to AI governance frameworks, model risk management, red-teaming, or DPIA-style assessments. • Domain experience in a regulated industry (e.g., financial services, insurance/reinsurance, healthcare) is a plus. • Contributions to open-source projects, publications, or a portfolio of applied AI work. Education & Certifications • Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Mathematics, Statistics • Relevant certifications are advantageous, for example: • Cloud/ML certifications (e.g., Azure AI Engineer / Azure Data Scientist, AWS Machine Learning, Google Cloud ML Engineer). • Databricks ML / Data Engineer certifications. • Vendor or framework-specific GenAI / agentic AI credentials. Tools & Technologies • Languages: Python (primary); SQL • GenAI / LLMs: OpenAI, Anthropic (Claude), Azure OpenAI, open-weight models (e.g., Llama, Mistral); LangChain / LangGraph / LlamaIndex; MCP for tool and context integration. • Vector / Retrieval: FAISS, Pinecone, Weaviate, pgvector, or similar. • Data & Compute: Spark, Databricks, Pandas, Delta Lake; feature stores. • Cloud & ML Platforms: Azure ML or AWS SageMaker, or Google Vertex AI. • ML / DL: scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers. • MLOps & DevOps: MLflow, Docker, Kubernetes, CI/CD pipelines, Git; experiment tracking and model registry. • Monitoring & Observability: drift/performance monitoring, evaluation harnesses, logging and alerting tooling. Nice-to-Have / Differentiators • Experience taking AI systems from prototype to production with safety controls (guardrails, prompt-injection defenses, kill-switches, human-in-the-loop review). • Track record of optimizing inference cost and latency for LLM-based applications at scale. • Experience designing evaluation frameworks for non-deterministic GenAI outputs (quality, factuality, safety). • Familiarity with enterprise AI governance, model documentation, and audit/compliance requirements. • Strong product sense — understanding how AI features create user and business value, not just model metrics. • Experience integrating AI into existing enterprise systems and workflows with minimal disruption.