AI Automation Engineer

Costa Rica · Posted Oct 12, 2026

At a glance

Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.

We're hiring an AI Automation Engineer to build the next generation of IT automations to improve the employee experience at Databricks.
You'll be building production-grade AI agents and automation services that understand employee requests, retrieve enterprise context, reason across systems, invoke tools and APIs, and take action safely.

Build AI agents and automations: Design, deploy, and run AI agents that support all of our employees.
Maintain MCP servers across our AI tools portfolio.
Build agentic workflows that decompose goals into steps, handle failures, pause for human approval as needed, and resume their workflows.
Ground agents in trusted internal knowledge (policies, runbooks, system documentation, SOPs, etc).
Turn high-volume support CUJs into self-service experiences by identifying repetitive, high-friction IT interactions and converting them into reliable AI automation experiences.
Instrument AI automations with logs, metrics, and feedback loops.
Design and implement AI automations with identity governance, tools RBAC, human-in-the-loop approvals where applicable, audit trails, and failure handling.
Create shared automation infrastructure (credential vaults, event ingestion, scheduling, approval workflows, cache/state stores) that other teams can build on.
Work with IT Support, Employee Experience, Security, IAM, Infrastructure, and Engineering to identify automation opportunities and take solutions from prototype to production.
Write and maintain high-quality technical/engineering documentation that clearly communicates system designs, operational procedures, and architectural decisions.

Proficiency in Python.
Experience with Git-based development, automated testing, and CI/CD.
Strong experience with REST APIs, OAuth2/authentication frameworks, event-driven integrations, and enterprise system integrations.
Shipped production LLM-powered applications or AI agents.
Experience with agent patterns: tool/function calling, multi-step workflows, context management, RAG, structured outputs, and plan decomposition.
Understanding of LLM failure modes: hallucination, incorrect tool use, ambiguous intent, prompt injection, excessive permissions, and non-deterministic behavior.
Experience evaluating and improving AI systems using automated evals, traces, test datasets, human feedback, or similar approaches.
Ability to translate ambiguous employee friction problems into simple, reliable technical solutions.
Strong judgment around security, privacy, least privilege, and safe automation in an enterprise environment.
Strong collaboration and communication skills.

Experience building agents on Databricks (Agent Bricks) or with frameworks such as OpenAI Agents SDK or LangGraph/LangChain.
Experience with MLflow tracing/evaluation, vector search, and LLM observability.
Experience integrating IT platforms: Okta/Microsoft Entra ID, Google Workspace, Jira, Slack, Jamf, GitHub, Workday, or SaaS administration APIs.
Familiarity with identity lifecycle, access management, device management, and software provisioning.
Experience with automation or integration platforms (Tines, MuleSoft, Workato, etc.).
Experience with MCP (Model Context Protocol) server development.

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