- 5+ years of advanced professional Python development experience with production-grade coding practices.
- Strong hands-on experience writing clean, tested, maintainable code using typing, pytest, packaging standards, Git, CI/CD, Docker, and code review discipline.
- 1.5+ years of hands-on experience building GenAI / LLM applications in production using OpenAI, Azure OpenAI, Anthropic, Bedrock, or similar model APIs.
- Experience building LLM-powered agents, including intent classification, domain-specific agents, agentic extraction flows, and multi-step orchestration.
- Hands-on experience with Agentic AI frameworks or patterns such as LangChain, LangGraph, LlamaIndex, function calling, tool use, or custom orchestration.
- Strong experience in structured output extraction from LLMs using JSON schema enforcement, Pydantic, retry/repair strategies, and validation logic.
- Experience with RAG and vector search concepts including embeddings, chunking, hybrid search, reranking, entity resolution, fuzzy matching, confidence thresholds, and disambiguation flows.
- Working knowledge of vector databases or search platforms such as pgvector, Pinecone, Weaviate, OpenSearch, Snowflake vector functions, or equivalent.
- Strong API development experience using FastAPI or similar frameworks such as Flask or Django.
- Experience with async Python and orchestration of parallel LLM/API calls.
- Strong SQL skills and comfort working with large analytical datasets.
- Cloud environment experience, preferably Azure; AWS or GCP acceptable.
- Strong written and verbal communication skills, with ability to collaborate directly with engineering teams, product owners, and cross-functional stakeholders.
Roles & Responsibilities
- Build and enhance LLM-powered agentic applications using Python.
- Develop intent classification, domain-specialist agent workflows, and agentic extraction pipelines for complex user questions.
- Build natural-language-to-structured-payload pipelines that convert user utterances into accurate JSON query payloads, including filters, exclusions, rankings, and metric selection.
- Integrate semantic search and vector retrieval services for embedding-based entity resolution, fuzzy matching, confidence scoring, and disambiguation flows.
- Design, build, and consume FastAPI-based microservices.
- Implement async orchestration patterns for parallel LLM calls, API calls, and downstream service integrations.
- Develop prompt engineering patterns, structured output enforcement, JSON schema validation, function calling, tool usage, and guardrails.
- Build evaluation harnesses, test sets, and regression test frameworks to measure extraction accuracy and validate prompt/model changes.
- Work with metadata/catalog services, entitlement-aware data access, and reporting-engine payload contracts.
- Collaborate with multiple platform, data, service, and product teams to deliver production-ready GenAI capabilities.
- Write clear technical documentation including Confluence pages, ADRs, sequence diagrams, and flow diagrams.
- Participate in hands-on technical evaluation, code reviews, design discussions, and production readiness reviews.
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