DE&A - AIML - Data Science - Artificial Intelligence of Things (AIOT)

Key Responsibilities Design and develop enterprise RAG applications using LLMs, embeddings, vector databases, and hybrid search. Build end-to-end document ingestion and knowledge ingestion pipelines for structured and unstructured data. Implement document parsing, chunking, metadata enrichment, embeddings, indexing, and retrieval strategies. Design and optimize semantic, vector, keyword, and hybrid search solutions. Develop RAG workflows incorporating query understanding, query rewriting, retrieval, reranking, context generation, and response generation . Work with LLMs such as Azure OpenAI, Anthropic Claude, or equivalent models . Develop agentic AI solutions using tools, function calling, MCP, and multi-agent/single-agent architectures where appropriate. Implement RAG evaluation and observability including retrieval quality, answer relevance, groundedness, hallucination detection, latency, and token/cost monitoring. Optimize RAG applications for accuracy, latency, scalability, and cost . Integrate RAG applications with enterprise systems, APIs, databases, repositories, and knowledge sources. Develop secure APIs and backend services for AI applications. Collaborate with architects, developers, business analysts, and domain experts to translate business requirements into AI solutions. Establish best practices around prompt engineering, context management, guardrails, security, and responsible AI . Troubleshoot production issues and continuously improve the AI application based on user feedback and evaluation metrics. Required Technical Skills Generative AI / LLM Strong understanding of LLMs and Generative AI Prompt engineering and structured prompting LLM inference and model selection Function calling / tool calling Context-window management Understanding of hallucination and grounding challenges RAG Strong hands-on experience building RAG applications Document ingestion and preprocessing Chunking strategies Metadata design and filtering Embedding generation Vector search Hybrid search Reranking Query expansion / rewriting Retrieval optimization RAG evaluation AI / Agentic Frameworks Experience with one or more frameworks such as: LangGraph Google ADK Experience with MCP (Model Context Protocol) is a plus. Understanding of agent orchestration and tool-based workflows. Cloud & Search Strong experience with Microsoft Azure Azure OpenAI / Azure AI Foundry Azure AI Search or equivalent vector search platform Azure Blob Storage Azure App Service / Functions API Management Experience with AWS AI services or Amazon OpenSearch is a plus. Programming Strong Python development skills REST API development Flask / FastAPI JSON and API integrations Experience with SQL and relational databases Databases / Search Vector databases/search engines such as: Azure AI Search OpenSearch PostgreSQL/pgvector Pinecone Elasticsearch Weaviate Understanding of indexing and search optimization. RAG Evaluation & Observability Experience with AI observability and evaluation tools such as: Arize Phoenix LangSmith Azure AI evaluation capabilities RAGAS Custom evaluation frameworks Knowledge of metrics such as: Context relevance Context precision/recall Answer relevance Faithfulness / groundedness Retrieval accuracy Hallucination rate Latency Token consumption Cost per request Preferred / Good-to-Have Skills Experience with Guidewire PolicyCenter, ClaimCenter, BillingCenter , or other enterprise insurance platforms. Experience working with large technical documentation repositories. Understanding of Guidewire data models, APIs, configuration, and data dictionaries. Experience building AI assistants for enterprise developers. Experience with structured knowledge extraction from HTML, XML, JSON, PDFs, source code, database schemas, and technical documentation . Knowledge of enterprise security, RBAC, PII protection, and data governance. Experience with semantic caching and .