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

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

• 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.