Building and deploying AI agents and agentic workflows using the client's internal agent framework and modern orchestration tools (LangChain, LangGraph, CrewAI, AutoGen, or similar)
Designing and implementing RAG pipelines
including chunking strategies, embeddings, and vector store integrations (Pinecone, Weaviate, pgvector)
Processing and structuring financial documents (PDFs, DOCX reports, tables, CIM/DDQ materials) into clean, machine-readable outputs via Python
Integrating REST APIs and cloud services to connect agent workflows with existing business systems and data infrastructure
Owning the full delivery cycle for each initiative: scoping, development, testing, deployment, and handover to business users
Instrumenting agents for observability, writing test harnesses for non-deterministic behaviour, and ensuring failures are explicit and handled gracefully
Communicating proactively with the client-side team
surfacing blockers early, documenting solutions clearly, and keeping stakeholders in the loop without being asked
Requirements
3-5 years of Python development experience
Hands-on experience building agentic AI workflows using LLM orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen, or similar)