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.

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

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.

Strong experience with Microsoft Azure

Azure AI Search or equivalent vector search platform

API Management
Experience with AWS AI services or Amazon OpenSearch is a plus.

Strong Python development skills
REST API development

JSON and API integrations
Experience with SQL and relational databases

Vector databases/search engines such as:

OpenSearch
PostgreSQL/pgvector
Pinecone
Elasticsearch
Weaviate
Understanding of indexing and search optimization.

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

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.

with semantic caching and

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

Job Fit Check

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Key Responsibilities

Generative Ai And Llm

Rag

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Ai And Agentic Frameworks

Cloud & Search

• Azure OpenAI And Azure AI Foundry

• Azure App Service And Functions

Programming

• Flask And FastAPI

Databases And Search

• Azure AI Search

Rag Evaluation & Observability

Preferred And Good-To-Have Skills

Experience

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