We are looking for a Senior AI Engineer to architect, develop, and launch an AI-driven chatbot/agent on Databricks, following a design similar to an existing internal tool (LangGraph/LangChain orchestration combined with vector database semantic search), but tailored to a new business scenario. The right candidate will be able to operate autonomously, infer and adapt concepts from previous builds, and take full ownership of the project from initial prototype to full production release. Responsibilities Architect and develop a conversational AI/agentic application leveraging LangChain and/or LangGraph, hosted on Databricks Build semantic search and retrieval-augmented generation (RAG) pipelines using vector embeddings paired with a vector database (such as Databricks Vector Search, Chroma, Pinecone, or FAISS) Establish and refine similarity-matching logic, including embedding model selection, distance metrics, thresholds, and ranking of "% match" results Connect to various data sources (Databricks Unity Catalog tables, Delta Lake, APIs) to build and prepare the knowledge base/corpus Manage the complete ML/LLMOps lifecycle, covering experimentation, evaluation, versioning, deployment, and production monitoring of the chatbot Partner with business stakeholders to convert new use cases into clear technical specifications Produce clean, well-documented, maintainable Python code while setting up testing and CI/CD workflows for the application Guarantee proper data privacy practices, access control, and cost oversight (covering token usage and compute resources) Requirements A minimum of 3 years in software/ML engineering, including at least 1 year working with LLM-based applications Strong Python skills, covering the ability to build, debug, and refactor production-level code Deep knowledge of LLM orchestration frameworks like LangChain and/or LangGraph, encompassing agent design, chains, tool calling, and state/memory handling Hands-on experience with vector databases, embeddings, and semantic search, including generating embeddings, indexing within a vector store, and building similarity search or RAG retrieval systems Working knowledge of the Databricks platform, including notebooks, jobs, clusters, and Unity Catalog Practical experience integrating and prompt-engineering with LLM providers such as OpenAI, Anthropic, and Azure OpenAI A Bachelor's degree in Computer Science, Data Science, Engineering, or a related discipline (or equivalent hands-on experience) Excellent communication abilities, capable of navigating ambiguous requirements with minimal documentation support English language skills at B2 level or above Nice to have Exposure to MLOps/LLMOps tools such as MLflow, along with model/prompt versioning and LLM output evaluation frameworks Experience in API/backend development using frameworks like FastAPI or Flask to deploy the chatbot as a service Understanding of front-end/chat interface integration, connecting a backend agent to a chat UI
