Background in software engineering with at least five years of experience, the last two spent building and operating production AI systems. Shipped agentic workflows, RAG pipelines, or LLM-powered applications to real users. Strong Python fundamentals across APIs and async systems, which is the foundation the AI work sits on. Comfortable picking the practical solution over the clever one.
Fluent in LangChain, LangGraph, PydanticAI, or AutoGen, with hands-on experience with vector databases, retrieval evaluation, and observability tooling such as LangSmith, RAGAS, Arize, or Langfuse. Prior fintech or banking experience is a genuine advantage, not a checkbox.
• 5+ years software engineering; 2+ years building and shipping production agentic AI or RAG systems
• Agent framework experience: LangChain, LangGraph, PydanticAI, AutoGen, or Semantic Kernel
• RAG stack proficiency: embedding models, vector DBs (Pinecone, Weaviate, Milvus, FAISS), hybrid search, retrieval evaluation
• LLM integration depth: tool calling, structured outputs, multi-step reasoning, behavioral regression testing
• AI eval and observability tooling: LangSmith, RAGAS, DeepEval, Arize, Langfuse, or equivalent
• REST APIs, async Python, microservices; Azure cloud experience preferred