Senior GenAI/Python Engineer

Apptad Inc; Chicago (IL); On-site; USD 140,000 - 190,000; Full time

Job summary

Apptad Inc. in Chicago, IL seeks a senior Python developer to build GenAI-powered agentic applications. You will design domain-specific agents, extract structured payloads, and integrate vector search and semantic retrieval into production services.

You will implement robust JSON schemas, testing, and async orchestration within FastAPI-based microservices, collaborating with cross-functional teams to deliver scalable GenAI capabilities.

Qualifications

• 5+ years of Python development experience.
• Strong tested code with typing, pytest, packaging, Git, CI/CD, Docker.
• 1.5+ years building GenAI/LLM apps in production.
• Experience building LLM-powered agents and multi-step orchestration.
• Experience with LangChain/LlamaIndex or similar.
• Strong JSON schema validation and error handling.
• SQL skills for large analytical datasets.

Responsibilities

• Build and enhance LLM-powered agentic applications using Python.
• Develop intent classification and workflows for domain-specific agents.
• Create pipelines converting user utterances to structured JSON payloads.
• Integrate semantic search and vector retrieval for entity resolution.
• Design, build, and consume FastAPI-based microservices.
• Implement async orchestration for parallel LLM/API calls.
• Develop prompt engineering patterns, validation, and guardrails.
• Collaborate with platform, data, service, and product teams.
• Write technical docs and participate in code reviews.

Skills

Python

GenAI/LLM

Async Python

JSON schema

Unit testing

Docker

CI/CD

SQL

Git

OpenAI

FastAPI

Tools

LangChain

LlamaIndex

Weaviate

Pinecone

pgvector

OpenSearch

Job description

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.