SQL and Python Engineers

As an AI Engineer at NTT DATA, you will be responsible for designing, building, and deploying AI-powered solutions that directly support the P&C insurance operations. You will work closely with data engineering, analytics, and business teams to deliver LLM-powered applications, automated AI agents, and production-ready ML pipelines across claims, underwriting, and actuarial domains. This hands-on role requires you to be comfortable moving from architecture whiteboard to working code efficiently. Key Responsibilities: - 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 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 against insurance-specific tasks to guide platform selection. - 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. - 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 or Azure Container Apps, ensuring scalability and low-latency response. - Collaborate with data engineering teams to ensure feature pipelines are production-grade, versioned, and integrated with the Feature Store on Databricks or Azure ML. Qualifications Required: - Bachelor's degree in Computer Science, Data Science, Machine Learning, Software Engineering, or a related quantitative field. Master's degree is a plus. - 35 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. About NTT DATA: NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. Committed to accelerating client success and positively impacting society through responsible innovation, NTT DATA is one of the world's leading AI and digital infrastructure providers. With unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers, and application services, NTT DATA's consulting and industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, NTT DATA has experts in more than 50 countries and offers clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D. As an AI Engineer at NTT DATA, you will be responsible for designing, building, and deploying AI-powered solutions that directly support the P&C insurance operations. You will work closely with data engineering, analytics, and business teams to deliver LLM-powered applications, automated AI agents, and production-ready ML pipelines across claims, underwriting, and actuarial domains. This hands-on role requires you to be comfortable moving from architecture whiteboard to working code efficiently. Key Responsibilities: - 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 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 against insurance-specific tasks to guide platform selection. - Architect and implement autonomous AI agents