AI Engineer (Generative AI / MLOps / AI Agents)

Job Title:

AI Engineer (Generative AI / MLOps / AI Agents) Location : Warren NJ- Hybrid role

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.