AI Automation Engineer
Databricks is a data and AI platform that lets organizations build analytics, AI agents, and applications on a unified, governed lakehouse.
Funding history
Investors
About Databricks
Data engineers, analysts, and AI teams use Databricks to process large datasets, build reliable pipelines, and train models on a single governed platform. Users can run SQL analytics, serve ML predictions in real time, and deploy AI agents grounded in enterprise data. Its open lakehouse architecture provides consistent security and governance across analytical and operational workloads.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You'll build production-grade AI agents and automation services that understand employee requests, retrieve enterprise context, invoke tools and APIs, and take action safely. You'll reimagine how employees interact with IT through agentic experiences, working with many teams to take solutions from prototype to production.
Requirements
- Proficiency in Python or Go (3 years)
- Experience with Git-based development, automated testing, and CI/CD
- Strong experience with REST APIs, OAuth2, event-driven and enterprise integrations
- Shipped production LLM-powered applications or AI agents
- Experience with agent patterns: tool calling, multi-step workflows, context management, RAG, structured outputs
- Understanding of LLM failure modes
- Experience evaluating and improving AI systems
- Strong judgment around security, privacy, and least privilege
- Strong collaboration and communication skills
- Experience with automation or integration platforms (Tines, MuleSoft, Workato)
- Experience with MCP server development
Responsibilities
- Design, deploy, and run AI agents that support employees
- Maintain MCP servers across the AI tools portfolio
- Build agentic workflows that decompose goals, handle failures, pause for human approval, and resume
- Ground agents in trusted internal knowledge
- Turn high-volume support journeys into self-service experiences
- Instrument AI automations with logs, metrics, and feedback loops
- Implement identity governance, tools RBAC, approvals, audit trails, and failure handling
- Create shared automation infrastructure for other teams
- Work with IT Support, Security, IAM, Infrastructure, and Engineering to identify automation opportunities
- Write and maintain technical documentation