AI Platform Engineer

Key Responsibilities:

Architect AI Agent Infrastructure: Design provision and maintain the cloud foundations required for enterprise AI agents using Vertex AI Agent Builder (Gemini Enterprise Agent Platform).

Infrastructure as Code (IaC): Maintain a strict IaC philosophy ensuring 100% of the platforms infrastructurefrom Agent Engine runtimes to VPCs and Cloud Storage Data Storesis declared and managed via Terraform.

Automate Everything: Build optimize and maintain robust secure GitLab CI/CD pipelines for automated agent testing deployment and configuration pinning.

Data & Grounding Management: Configure and optimize data ingestion pipelines (Vertex AI Search Data Stores) to ground agents effectively on enterprise data.

Security & IAM Governance: Own the complex IAM structures Service Accounts and cryptographic Agent Identities required to secure agent tool-calling and enterprise data access.

Observability & Cost Optimization: Implement logging monitoring and tracing loops for agent reasoning while keeping a sharp eye on token spend and runtime costs. Core Technical Requirements Must-Haves

GCP AI Ecosystem: Hands-on experience with Vertex AI Agent Builder (or Gemini Enterprise Agent Platform components like Agent Studio Agent Development Kit (ADK) and Agent Engine).

Advanced Terraform: Deep knowledge of writing reusable modular Terraform code to manage complex cloud environments IAM policies and managed services.

CI/CD Expertise: Proven track record of configuring complex GitLab pipelines utilizing runners environment staging caching and automated testing blocks.

Core GCP Architecture: Solid understanding of baseline GCP infrastructure including GKE Cloud Run VPC/Shared VPC networking Identity-Aware Proxy (IAP) and Cloud Storage.

Scripting & Orchestration: Strong programming skills in Python (preferred for Agent ADK work) or Go.

Nice-to-Haves

Experience with LLM frameworks like LangChain LlamaIndex or Googles native Agent SDK.

Familiarity with vector databases (Vertex AI Vector Search Pinecone or pgvector).

GCP Professional Cloud Architect or Professional DevOps/MLOps Engineer certifications