We are seeking a hands-on Senior AI Engineer who designs, builds, and operates production GenAI systems - agentic workflows, RAG pipelines, and LLM-backed services with real users and real SLAs. This is an engineering role, not a research role. The bar is reliability, latency, cost, observability, and safe deployment at scale, with end-to-end ownership from architecture through on-call. Typical workloads include enterprise knowledge platforms, conversational analytics, agentic automation, and LLM-augmented data products. Responsibilities Design agent orchestration (graph/state, conditional routing, tool calling, memory, checkpointing) in LangGraph / LangChain or equivalent Build production RAG end-to-end: chunking, embeddings, vector stores, hybrid retrieval, reranking, caching, and grounded synthesis Own Python / FastAPI services - async, SSE streaming, session handling, and structured error contracts Instrument with tracing and evaluation harnesses (MLflow, OpenTelemetry, or equivalent) for accuracy, cost, and regression Ship on Docker + Kubernetes (EKS/AKS/GKE) via CI/CD with test, eval, and canary gates Drive LLM cost engineering - model routing, prompt optimization, caching, token accounting, and build-vs-buy decisions Apply GenAI safety & governance: hallucination control, prompt-injection defense, PII handling, and HITL where required Partner with data engineering on semantic layers and pipelines (PySpark / SQL where applicable) Requirements 5+ years in software engineering, with 2+ years shipping production LLM / agentic systems (not POCs or research) Proficiency in Python and FastAPI (async, REST, SSE) Production expertise in LangChain and LangGraph (or equivalent serious production experience with LlamaIndex, AutoGen, or MCP stacks) Background in production RAG: embeddings, chunking, and hybrid retrieval with reranking and caching Skills in vector databases such as Pinecone, Weaviate, pgvector, OpenSearch, or Databricks Vector Search Knowledge of at least one major LLM provider in production - AWS Bedrock (preferred), OpenAI / Azure OpenAI, or Anthropic - with model selection and routing trade-offs Competency in Kubernetes and Docker in real production environments (EKS/AKS/GKE) Expertise in cloud engineering on AWS Familiarity with observability and tracing tools (MLflow, LangSmith, OpenTelemetry), evaluation harnesses, and latency/cost ownership Capability to build CI/CD for AI systems (GitHub Actions, Jenkins, or equivalent) with test/eval gates Strong written and spoken English (B2 level); able to own design discussions with engineering and business stakeholders independently Nice to have Databricks depth - MLflow (tracking & serving), Vector Search, Unity Catalog / Metric Views, PySpark / SQL Experience with LLM fine-tuning - PEFT, LoRA, QLoRA Understanding of MCP servers and tool integration Qualifications in GenAI governance & FinOps - auditability, prompt-injection hardening, PII, and token cost in regulated environments Background in classical ML / DL - NLP, BERT-family, time-series, and CV