Senior GenAIPython Engineer

Must Have Technical/Functional Skills

• 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.