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About the role
Design, develop, and deploy goal-oriented AI agents and full agentic systems using Google ADK and n8n , automating high-impact workflows inside operational teams.
Partner directly with non-technical stakeholders — sit with operations teams, understand their pain points, translate workflows into agentic solutions, and explain your design choices in language they actually understand. This is roughly half the job.
Be the team's Python referent. You will own the production codebase, enforce strong software engineering principles (unit tests, CI/CD, Git, code review), and bring AIOps/AgentOps best practices to the team (observability, versioning of agent blueprints, eval pipelines).
Ship complete agentic systems end-to-end — orchestration, evaluation, guardrails, prompt management, and integration with internal data sources via MCP servers that you'll build yourself when needed, or partner with other R&D teams when they own the system. If a partner team doesn't have bandwidth, you should be comfortable picking up the integration work directly.
Detect platform-level gaps (reliability, latency, observability, model routing) and formulate clear, prioritized requests to the GenAI Core Platform team. You don't own the platform — but your production usage is what pushes it to get better.
Build a working expertise in agentic design patterns (eval, guardrails, multi-agent orchestration) and share it with internal AI champions and AI builders as they emerge across the company.
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