AI/ML Developer

Role: AI/ML Developer

Location: St. louis, MO- Onsite

JD:

We are looking for a skilled AI/ML Developer to join our agentic platform team. You will design, develop, and deploy LLM-powered agents and pipelines that directly serve field technicians in production. You will work closely with the platform architect and contribute to the core multi-agent orchestration system.

Key Responsibilities

• Design and develop LLM-powered agents using LangGraph and LangChain frameworks
• Build and enhance multi-agent orchestration pipelines with conditional routing, state management, and error recovery
• Develop RAG pipelines with hybrid search (semantic + keyword), re-ranking, and Reciprocal Rank Fusion
• Implement tool-calling agents with parallel API execution, sequential enrichment chains, and summarization
• Build real-time streaming capabilities (WebSocket / SSE) for conversational AI Integrate with Azure OpenAI APIs with circuit breaker patterns and fallback chains
• Implement prompt engineering and dynamic prompt management (DB-backed with in-memory caching)
• Design and implement multi-modal AI features (image analysis with vision models)
• Write production-quality Python code with error handling, logging, observability, and unit tests
• Participate in code reviews, architecture discussions, and sprint delivery

Required Skills & Experience

Must-Have

• 10+ years of software development with strong Python expertise in production environments
• 2+ years hands-on LLM development (LangChain, LangGraph, or equivalent frameworks)
• RAG pipeline experience - embeddings, vector search, re-ranking, answer generation
• LLM APIs - OpenAI Chat Completions, Responses API, streaming, tool calling
• Async Python - asyncio, httpx, FastAPI
• Prompt engineering - system prompts, few-shot, structured JSON output
• Circuit breaker patterns, retry logic, resilient service design
• PostgreSQL and async ORMs (SQLAlchemy async / Alembic)
• Git, CI/CD, Docker, Kubernetes basics
• Strong system design skills - microservices, distributed systems, API design

Nice-to-Have

• LangGraph StateGraph (conditional edges, node pipelines, compiled graphs)
• Multi-agent systems - orchestration, routing, RBAC-based access control
• Langfuse or similar LLM observability (tracing, token usage, latency)
• WebSocket and SSE streaming protocols
• Multi-modal AI (vision models, image analysis)
• Vector databases (OpenSearch, Pinecone, pgvector)
• AWS services (Aurora PostgreSQL, SSM, IAM, EKS)
• Telecommunications or field service domain experience