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
.
Job description provided by Zensar.
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