Insurance GenAI & LLM Engineer

TechDigital Group; New Jersey; On-site; USD 90,000 - 130,000; Full time

Job summary

A technology consulting firm based in New Jersey is seeking an AI/ML Engineer to design, fine-tune, and deploy Large Language Models (LLMs) for insurance applications. The role involves building autonomous AI agents and establishing robust MLOps pipelines for model deployment and monitoring. Ideal candidates will have 3–5 years of experience in AI/ML engineering, including hands-on work with LLMs and cloud environments, particularly Azure. The firm fosters collaboration with business analysts and underwriters to create effective AI solutions.

Qualifications

• 3–5 years of professional experience in AI/ML engineering, delivering production-grade AI systems.
• Hands-on experience building and deploying LLM-powered applications.
• Proven experience implementing MLOps pipelines in cloud environments, preferably Azure.
• Experience developing AI agents or automation workflows.

Responsibilities

• Design, deploy, and fine-tune LLMs for insurance use cases.
• Build autonomous AI agents capable of complex decision-making.
• Architect end-to-end MLOps pipelines for model management.
• Collaborate with domain experts to translate requirements into AI solutions.

Skills

AI/ML engineering

Large Language Models (LLMs)

MLOps

Prompt engineering

Agile methodology

Tools

Azure ML

Databricks

MLflow

Azure DevOps

LangChain

LlamaIndex

Job description

Key Responsibilities

Generative AI & LLM Engineering

• Design, fine-tune, and deploy Large Language Models (LLMs) for insurance-specific use cases including document intelligence, claims summarization, policy interpretation, and underwriting Q&A.
• Build Retrieval-Augmented Generation (RAG) pipelines using vector databases (e.g., Azure AI Search, Pinecone, ChromaDB) to ground LLM outputs in enterprise knowledge bases.
• Develop prompt 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 via LangChain, 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 implement autonomous 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 as ReAct, Chain-of-Thought, and Tool-Augmented Agents to handle complex, multi-turn insurance workflows.
• Design human-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 as Azure 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 maintain end-to-end MLOps pipelines covering model training, versioning, validation, deployment, and monitoring using MLflow, Azure ML, and Databricks.
• Implement CI/CD pipelines for ML models using Azure DevOps or GitHub Actions, enabling reliable, repeatable model releases.
• Deploy models as REST APIs or batch inference services on Azure Kubernetes Service (AKS) or Azure Container Apps, ensuring scalability and low-latency response.
• Establish model monitoring frameworks to detect data drift, model degradation, and prediction anomalies in production.
• Manage the model 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 the Feature 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 in Agile/Scrum ceremonies including sprint planning, standups, and retrospectives as an active delivery contributor.
• Produce clear, well-structured technical documentation including solution designs, API specs, model cards, and deployment runbooks.
• Mentor junior engineers and contribute to internal AI engineering best practices and standards

Experience

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