Description

You will design, fine-tune, and deploy Large Language Models (LLMs) for insurance-specific use cases including document intelligence, claims summarization, and underwriting Q&A.

This role is hybrid.

Responsibilities

• Build Retrieval-Augmented Generation (RAG) pipelines using vector databases to ground LLM outputs in enterprise knowledge bases.
• Architect and implement autonomous AI agents capable of multi-step reasoning and tool use for workflows like FNOL triage and compliance checks.
• Develop prompt engineering frameworks and systematic evaluation pipelines to ensure LLM output quality and safety in regulated contexts.
• Build and maintain end-to-end MLOps pipelines covering model training, versioning, deployment, and monitoring using MLflow and Azure ML.
• Deploy models as REST APIs or batch inference services on Azure Kubernetes Service (AKS), ensuring scalability and low-latency response.

Required Skills

• 5+ years of professional experience in AI/ML engineering with production-grade AI systems.
• Hands-on experience building LLM-powered applications using LangChain, LlamaIndex, or Semantic Kernel.
• Proven experience implementing MLOps pipelines in cloud environments, specifically Azure.
• Experience developing AI agents or automation workflows using agentic frameworks.
• Proficiency in Python, SQL, T-SQL, Docker, and Kubernetes.
• Experience integrating LLM capabilities with internal data platforms and enterprise systems.
• Strong understanding of REST and GraphQL APIs for integrating AI services.

Preferred Skills

• Prior experience in financial services, insurance, or regulated industries.
• Master's degree in Computer Science, Data Science, Machine Learning, or a related quantitative field.

Key Skills

Education

Bachelor's degree