AI Engineer - Generative AI/ LLM

Generative AI & LLM Engineering

• Design, fine-tune, and deployLarge Language Models (LLMs) for insurance-specific use cases including document intelligence, claims summarization, policy interpretation, and underwriting Q&A.
• BuildRetrieval-Augmented Generation (RAG) pipelines using vector databases (e.g., Azure AI Search, Pinecone, ChromaDB) to ground LLM outputs in enterprise knowledge bases.
• Developprompt engineering frameworks and systematic evaluation pipelines to ensure LLM output quality, consistency, and safety in regulated insurance contexts.
• Integrate LLM capabilities with internal data platforms viaLangChain, LlamaIndex, or Semantic Kernel.
• Evaluate and benchmark foundational models (OpenAI GPT-4o, Azure OpenAI, Claude, Mistral, Llama) against insurance-specific tasks to guide platform selection.

AI Agents & Automation

• Architect and implementautonomous AI agents capable of multi-step reasoning, tool use, and decision-making for workflows such as FNOL triage, claims routing, policy lookup, and compliance checks.
• Build agentic frameworks using patterns such asReAct, Chain-of-Thought, and Tool-Augmented Agents to handle complex, multi-turn insurance workflows.
• Designhuman-in-the-loop (HITL) checkpoints and escalation logic to ensure AI agents operate within defined risk and compliance boundaries.
• Integrate agents with internal APIs, data platforms, and enterprise systems using orchestration tools such asAzure Logic Apps, Apache Airflow, or Databricks Workflows.
• Develop guardrails, monitoring, and audit logging for all deployed agents to meet regulatory and governance standards.

MLOps & Model Deployment

• Build and maintainend-to-end MLOps pipelines covering model training, versioning, validation, deployment, and monitoring usingMLflow, Azure ML, and Databricks.
• ImplementCI/CD pipelines for ML models using Azure DevOps or GitHub Actions, enabling reliable, repeatable model releases.
• Deploy models asREST APIs or batch inference services on Azure Kubernetes Service (AKS) or Azure Container Apps, ensuring scalability and low-latency response.
• Establishmodel monitoring frameworks to detect data drift, model degradation, and prediction anomalies in production.
• Manage themodel registry and lineage tracking to maintain governance and auditability of all AI assets.
• Collaborate with data engineering teams to ensure feature pipelines are production-grade, versioned, and integrated with theFeature Store on Databricks or Azure ML.

Collaboration & Delivery

• Work closely with business analysts, actuaries, underwriters, and claims professionals to translate domain requirements into AI solution designs.
• Participate inAgile/Scrum ceremonies including sprint planning, standups, and retrospectives as an active delivery contributor.
• Produce clear, well-structuredtechnical documentation including solution designs, API specs, model cards, and deployment runbooks.
• Mentor junior engineers and contribute to internal AI engineering best practices and standards

• 3–5 years of professional experience in AI/ML engineering, with demonstrated delivery ofproduction-grade AI systems.
• Hands-on experience building and deployingLLM-powered applications using frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
• Proven experience implementingMLOps pipelines in cloud environments (Azure preferred).
• Experience developingAI agents or automation workflows using agentic frameworks.
• Prior experience infinancial services, insurance, or regulated industries is strongly preferred.