AI Platform Engineer
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What you'll do
- 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 platform’s infrastructure—from Agent Engine runtimes to VPCs and Cloud Storage Data Stores—is 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.
What they're looking for
- 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 have
- Experience with LLM frameworks like LangChain, LlamaIndex, or Google's native Agent SDK.
- Familiarity with vector databases (Vertex AI Vector Search, Pinecone, or pgvector).
- GCP Professional Cloud Architect or Professional DevOps/MLOps Engineer certifications
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
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 platform’s infrastructure—from Agent Engine runtimes to VPCs and Cloud Storage Data Stores—is 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 Google's native Agent SDK.
● Familiarity with vector databases (Vertex AI Vector Search, Pinecone, or pgvector).
● GCP Professional Cloud Architect or Professional DevOps/MLOps Engineer certifications
Company
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