Description

• 5+ years of professional Python — advanced level, clean, tested, production-grade code (typing, pytest, packaging, code review discipline). • 1.5+ years hands-on building GenAI/LLM applications in production (not POCs only). OpenAI / Anthropic / Azure OpenAI / Bedrock or similar APIs. • Agentic frameworks and patterns: LangChain / LangGraph, LlamaIndex, or equivalent hand-rolled orchestration; tool/function calling; multi-step agent flows. • Structured output extraction from LLMs: JSON schema enforcement, Pydantic, retry/repair strategies. • RAG and vector search: embeddings, chunking strategies, hybrid search, reranking (any of pgvector, Pinecone, Weaviate, OpenSearch, or warehouse-native vector functionality). • FastAPI (or Flask/Django with strong API design), async Python, REST integration patterns. • SQL proficiency and comfort working against large analytical datasets. • Git, CI/CD, Docker; comfortable in cloud environments (Azure preferred; AWS/GCP acceptable). • Strong communication — these roles interact directly with multiple engineering teams and product owners. NICE-TO-HAVE • Snowflake (especially Cortex functions / Snowpark). • Experience with NL2SQL or NL-to-query/DSL systems. • Retail / CPG / market-measurement data domain exposure. • MCP (Model Context Protocol) or similar tool-integration standards. • LLM evaluation tooling (Ragas, promptfoo, custom eval harnesses); observability (LangSmith, Langfuse). • Prior experience in multi-agent systems with disambiguation/human-in-the-loop flows

Key Skills

Education

Bachelor's degree