Information Technology_USA - USA_Developer

2 Candidate Submittal Slots, New High Level PolicyBill Rate - MSP Owner: Rob FintonLocation: Chicago , IL (Remote or Hybrid)Duration: 6 monthsGBaMS ReqID: 10895010Competencies: 8-10+ years experience requiredDigital : Microsoft AzureDigital : PythonDigital : DevOps Continuous Integration and Continuous Delivery (CI/CD)Digital : SnowflakeGenerative AI, AI AgentsROLE: Senior Python / GenAI Engineer (2 positions, onshore)WHAT THEY WILL WORK ON- Building and extending LLM-powered agents in Python: intent classification, domain-specialist agents, and agentic extraction flows that decompose multi-part user questions- Natural-language-to-structured-payload pipelines: parsing user utterances into JSON query payloads (filters, exclusions, rankings, metric selection) with high precision- Integrating semantic search / vector retrieval services (embedding-based entity resolution, fuzzy matching, confidence thresholds, disambiguation flows)- Building and consuming FastAPI microservices; async orchestration of parallel LLM and API calls- Prompt engineering, structured output enforcement (JSON schema / function calling / tool use), and guardrails- Evaluation harnesses: building test sets, measuring extraction accuracy, regression-testing prompt and model changes- Working with metadata/catalog services, entitlement-aware data access, and reporting-engine payload contracts- Collaborating across multiple service teams; writing clear technical documentation (Confluence, ADRs, sequence/flow diagrams)MUST-HAVE SKILLS- 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 functions)- 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 ownersNICE-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