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 Google Cloud Platform 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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