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NTT DATA Logo NTT DATA · IT - Software Development

AI Engineer - Insurance Domain

United States, Warren Employment contract 87120.00 - 181500.00 USD / Year · Job Posted May 10, 2026
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Job Description

The AI Engineer role focuses on developing AI solutions for the insurance sector, emphasizing the design and deployment of Large Language Models and AI agents. Candidates should have at least 3 years of experience in AI/ML engineering, with a strong background in MLOps and cloud environments, preferably Azure. A bachelor's degree in a relevant field is required, while a master's degree is preferred. The position offers a competitive salary range of $87,120 - $181,500, depending on experience and qualifications.

Job Responsibility

  • 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

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
  • 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

Nice to have

  • 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
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