We are seeking a Senior AI Engineer to design, build, and deploy an AI-powered chatbot/agent on Databricks, similar in architecture to an existing internal tool (LangGraph/LangChain orchestration + vector database semantic search) but applied to a new business use case. The ideal candidate can work independently, reverse-engineer/inherit concepts from prior implementations, and own the solution from prototype through production deployment. Responsibilities Design and build a conversational AI / agentic application using LangChain and/or LangGraph, deployed on Databricks Implement semantic search / retrieval-augmented generation (RAG) pipelines using vector embeddings and a vector database (e.g., Databricks Vector Search, Chroma, Pinecone, FAISS) Define and tune similarity-matching logic (embedding models, distance metrics, thresholding, ranking of "% match" style results) Integrate with data sources (Databricks Unity Catalog tables, Delta Lake, APIs) to source and prepare the underlying knowledge base/corpus Own the ML/LLMOps lifecycle, including experimentation, evaluation, versioning, deployment, and monitoring of the chatbot in production Collaborate with business stakeholders to translate a new use case into technical requirements Establish clean, maintainable, well-documented Python code along with testing and CI/CD practices for the AI application Ensure appropriate handling of data privacy, access control, and cost management (token usage, compute) Requirements 3+ years of software/ML engineering experience, with at least 1 year working directly with LLM-based applications Proficiency in Python, with the ability to build, debug, and refactor production-grade code Expertise in LLM orchestration frameworks such as LangChain and/or LangGraph (agent design, chains, tool calling, state/memory management) Skills in vector databases, embeddings, and semantic search, including generating embeddings, indexing them in a vector store, and implementing similarity search or RAG retrieval Experience in LLM API integration and prompt-engineering against providers such as OpenAI, Anthropic, Azure OpenAI, or Databricks-hosted models Knowledge of PostgreSQL Bachelor's degree in Computer Science, Data Science, Engineering, or related field (or equivalent practical experience) Strong communication skills, with the ability to work with limited handoff documentation and ambiguous requirements Proficiency in English at a B2+ level Nice to have Familiarity with the Databricks platform, including notebooks, jobs, clusters, Unity Catalog, and Databricks Vector Search Background in MLOps/LLMOps tooling such as MLflow, model/prompt versioning, and evaluation frameworks for LLM outputs Skills in API/backend development using FastAPI, Flask, or similar, for exposing the chatbot as a service Experience with front-end/chat interface integration, wiring a backend agent to a chat UI
