Agentic AI Developer

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 clear 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 environments.
• 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.