Key Responsibilities

Core Responsibilities AI Agent Development
Design and develop AI agents and agentic workflows using orchestration frameworks (LangChain, LangGraph) for real business use cases.
Build end-to-end RAG pipelines — document ingestion, chunking, embedding, vector retrieval, reranking, and grounded response generation.
Translate business requirements into transparent AI system architectures, specifying automated functions versus those needing human review.
Evaluate AI solutions using appropriate retrieval and generation quality metrics; build lightweight eval harnesses and monitor for drift or degradation.
Apply MLOps fundamentals — experiment tracking, model versioning, and drift detection — to maintain production AI quality over time.




Ensure audit-defensible AI output design with full retrieval chain logging, appropriate for regulated financial settings.
Configure and integrate MCP (Model Context Protocol) frameworks for model context management and enterprise AI integration.
Deploy AI agents and services into production environments, including containerization with Docker and integration with enterprise API gateways and cloud infrastructure.
Collaborate with business and technical stakeholders to deliver and iterate on AI capabilities with measurable business value.
Document system architectures, deployment procedures, and operational runbooks for maintainability and knowledge transfer.

📌 Agentic Ai Manager Kolkata
🏢 PwC
📍 Kolkata