GCP AI Engineer / Google Cloud Agent Engineer
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- Build Gemini applications and agents on a production-ready Google Cloud foundation
- Ground agents in enterprise data and connect interoperable multi-agent systems
- Operate governed agents with evals, observability, security and reliability
Benchmark: Google Cloud Applied AI Agent Engineer + Data Engineering Agent / enterprise agent-platform engineering signals (current Sep 2026). Market refresh: Gemini Enterprise Agent Platform; Vertex AI/Gemini; ADK; MCP/A2A; BigQuery and enterprise grounding; Agent Runtime/Registry; evals/observability; Model Armor/identity; Cloud Run; production reliability.
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Build Gemini applications and agents on a production-ready Google Cloud foundation
Gemini + Vertex AI application engineering
Coverage: Gemini / Vertex AI / Model Selection / Structured Outputs / Fine-Tuning / Deployment
ADK, Agent Runtime + developer workflow
Coverage: Agent Development Kit / Gemini Enterprise Agent Platform / Agent Runtime / Cloud Run / Tool Calling
Ground agents in enterprise data and connect interoperable multi-agent systems
BigQuery, RAG + enterprise grounding
Coverage: BigQuery / Vertex AI Search & RAG / Cloud Storage / Cloud SQL / Vector Search / Data Agent Kit
MCP, A2A + Agent Registry orchestration
Coverage: Managed MCP Servers / Agent2Agent Protocol / Agent Registry / Enterprise APIs / Multi-Agent
Operate governed agents with evals, observability, security and reliability
Agent evaluation, observability + performance
Coverage: GenAI Evaluation / ADK Eval / Tracing / Quality Gates / Cost-Latency / SLOs
Model Armor, identity + production security
Coverage: Model Armor / Sensitive Data Protection / Agent Identity / Policy as Code / Cloud Run / Governance
Role-Readiness Proof / Capstone
Engineer a production Google Cloud agent for a real enterprise workflow. Build on Gemini/Vertex AI with a typed API or ADK service; ground it in BigQuery, Cloud Storage, Cloud SQL or Vertex AI Search; add at least two tools through Google-managed MCP or custom APIs; use A2A and Agent Registry for a multi-agent branch where useful; deploy through Agent Runtime or Cloud Run with least-privilege identity, Model Armor/SDP controls, observability, eval/regression gates, latency/cost targets and a rollback/runbook. Demonstrate a measurable business task from data retrieval through action.
Proof Rubric
- Prove correct Gemini/Vertex architecture
- grounded outputs and data permissions
- clean ADK/tool contracts
- working MCP/A2A integration where applicable
- eval quality and failure analysis
- actionable traces
- secure identity and Model Armor boundaries
- scalable deployment
- latency/cost discipline
- recovery from tool/model failures
- measurable task success
- and clear tradeoffs between managed Agent Platform, Cloud Run, retrieval options, single-vs-multi-agent and speed-vs-control