AI Platform Engineer

Tvh India Private Ltd; Pune District; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Tvh India Private Ltd seeks a seasoned AI infrastructure engineer to architect and operate enterprise AI agent platforms on Vertex AI.

You will own IaC with Terraform, build secure GitLab CI/CD pipelines, and optimize data grounding, IAM, and observability while controlling costs across cloud resources. Strong Python/Go skills and GCP familiarity are required for this role.

Location Pune, India; on-site collaboration with product teams.

Qualifications

• Hands-on experience with Vertex AI Agent Builder or Gemini Enterprise components.
• Advanced Terraform for modular, reusable code managing IAM and services.
• CI/CD experience with GitLab pipelines, runners, and environment staging.
• Knowledge of GCP architecture: GKE, Cloud Run, VPC, IAP, Cloud Storage.
• Programming skills in Python (preferred) or Go.

Responsibilities

• Architect AI agent infrastructure on Vertex AI platform.
• Maintain IaC for all platform components via Terraform.
• Build and optimize GitLab CI/CD pipelines for testing and deployment.
• Configure data ingestion pipelines for agent grounding.
• Manage IAM, service accounts, and agent identities securely.
• Implement observability, logging, and cost optimization.

Skills

Vertex AI

Terraform

GitLab CI/CD

GCP Architecture

Python

Go

IAM & Security

Observability

Tools

Agent Studio

ADK

Agent Engine

Job description

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