Responsibilities

Elicit, analyze, and document business requirements, translating them into clear functional specifications and user stories.
Design, develop, and support LLM-powered solutions, including RAG pipelines, prompt strategies, and retrieval workflows.
Build proof-of-concepts and collaborate closely with engineering teams using LangChain and LangGraph for orchestration.
Perform data analysis using Python and SQL to ensure data quality, relevance, and readiness for AI-driven solutions.
Design, optimize, and manage vector database schemas and retrieval strategies for efficient information access.
Develop and maintain BI dashboards using Power BI or Tableau to track performance metrics and generate actionable insights.
Apply prompt engineering best practices to ensure accuracy, grounding, safety, and reliability of LLM outputs.
Work within Agile delivery frameworks, managing backlogs, sprint planning, and stakeholder communication.
Ensure adherence to governance, security, and ethical AI standards across all solutions.

Requirements

Experience with LangSmith or other LLM observability and monitoring tools.
Strong understanding of hybrid search, re-ranking, and advanced retrieval techniques.
Familiarity with LLMOps/MLOps practices, including prompt versioning and model lifecycle management.
Exposure to Azure data platforms (Data Factory, Synapse, Databricks) and/or Snowflake.
Advanced business intelligence skills, including DAX, data modeling, and KPI design.
Solid understanding of embedding models, chunking strategies, and metadata-based filtering.
Domain experience in BFSI, Retail, Healthcare, or related industries is preferred.