Job Requirements

CLOUD AI SERVICES PRACTICE

- GOOGLE CLOUD

Cloud AI Engineer — Agentic AI & Generative AI (Google Cloud)

Experience: 15+ years We are looking for a senior engineer with 7—10 years of overall software/ML engineering experience, including at least 3 years building Agentic AI and Generative AI solutions on Google Cloud, to design, build, and deploy production-grade agentic and GenAI systems across the full AI stack.

Responsibilities

- Architect and build GenAI solutions using Vertex AI and Gemini, selecting models via Model Garden (Gemini, Claude, Gemma, Llama)
- Design and own Retrieval-Augmented Generation (RAG) pipelines using Vertex AI Embeddings, Vertex AI Vector Search, and Vertex AI Search / RAG Engine
- Lead development of production AI agents using the Agent Development Kit (ADK) on Vertex AI Agent Engine, with tool-calling via the Model Context Protocol (MCP) and multi-agent coordination via the Agent2Agent (A2A) protocol
- Define and enforce safety guardrails (Model Armor) and lead agent/GenAI evaluation before production release




- Own MLOps automation with Vertex AI Pipelines and drive tuning of Gemini and other models
- Mentor engineers and guide architecture decisions for agentic AI adoption across teams

Required Skills

- 15+ years overall software/ML engineering experience, including at least 3 years hands-on with Agentic AI and Generative AI specifically
- Deep hands-on experience with Vertex AI and/or the Gemini API in a production setting
- Strong grasp of embeddings, vector search, and Retrieval-Augmented Generation (RAG) architecture
- Practical experience with ADK, MCP tool-calling, and A2A multi-agent patterns
- Strong Python and Google Cloud fundamentals (Cloud Storage, IAM, Cloud Run)

Preferred

- Google Cloud Generative AI Leader or Professional Machine Learning Engineer certification
- Experience with MLOps, CI/CD, and production model monitoring at scale

Work Experience 15-30Years

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