AI Engineer - Insurance Domain - HYBRID

AI Engineer - Insurance Domain - HYBRID Location: Warren, New Jersey (US-NJ), United States (US) Day-to-Day 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. Education Bachelor's degree in Computer Science, Data Science, Machine Learning, Software Engineering, or a related quantitative field. Master’s degree is a plus. Minimum Requirements 3+ years of professional experience in AI/ML engineering, with demonstrated delivery of production-grade AI systems. 3+ years hands‑on experience building and deploying LLM‑powered applications using frameworks such as LangChain, LlamaIndex, or Semantic Kernel. 3+ years proven experience implementing MLOps pipelines in cloud environments (Azure preferred). 3+ years experience developing AI agents or automation workflows using agentic frameworks. 2+ years experience in financial services, insurance, or regulated industries is strongly preferred. Preferred Qualifications Experience with P&C insurance workflows such as FNOL processing, claims triage, underwriting decisioning, or actuarial modeling. Familiarity with insurance regulatory requirements including NAIC guidelines and data privacy standards (CCPA, GDPR). Experience implementing responsible AI principles — fairness, explainability, and bias mitigation — in regulated environments. Microsoft certifications: Azure AI Engineer Associate (AI-102) or Azure Data Scientist Associate (DP-100) preferred. Exposure to Data Mesh patterns and publishing AI model outputs as domain data products. Salary Range Where required by law, NTT DATA provides a reasonable range of compensation for specific roles. The starting pay range for this remote role is $87,120 - $181,500. EEO Statement NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement, please click here. If you’d like more information on your EEO rights under the law, please click here. For Pay Transparency information, please click here. #J-18808-Ljbffr NTT DATA