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AI Engineer - Insurance Domain

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NTT DATA

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Location:
United States , Warren

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Category:
IT - Software Development

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Contract Type:
Employment contract

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Salary:

87120.00 - 181500.00 USD / Year
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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

Additional Information:

Job Posted:
May 10, 2026

Employment Type:
Fulltime
Work Type:
Hybrid work
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