AI/ML Engineer

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

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