Google is hiring a Cloud AI Engineer to help strategic customers adopt Gemini Enterprise and the wider Google Cloud agentic AI product family. You will design production-ready architectures, build autonomous agents with Agent Development Kits, and work directly with technical teams to remove blockers during deployment. The role blends hands-on engineering with customer advisory work, so you will troubleshoot complex agent behaviour, present recommendations to business and technical stakeholders, and work with Google engineering teams on product challenges. Candidates can choose from four Indian office locations, and interviews are usually conducted in person.
This role suits engineers who enjoy building with large language models but also like sitting across the table from a customer and explaining why an architecture works. At Google Cloud, the job goes beyond writing code: you help real organisations turn generative AI experiments into agents that employees actually use every day.
Why This Role Matters
Most enterprises have already run a generative AI pilot. Far fewer have moved those pilots into secure, governed, high-adoption production systems. This position sits exactly at that gap. You will be the engineer who takes a customer’s workflow, such as document search, internal approvals or support triage, and turns it into an agent-driven solution built on Gemini Enterprise.
Success here is measured by adoption and reliability, not only by a working demo.
Because the customers are strategic accounts, your recommendations carry weight in how large organisations plan their AI rollout.
What You Will Actually Do
The core responsibilities fall into four connected areas, and the table below shows how they map to day-to-day work.
Area | What It Looks Like In Practice |
|---|---|
Delivery | Deploy and implement Gemini Enterprise solutions using Agent Development Kits (ADKs) for complex customer problems |
Advisory | Act as a trusted technical advisor and drive adoption among strategic customers |
Engineering Collaboration | Raise product challenges with Google engineering and provide workarounds where fixes take time |
Enablement | Deliver best-practice recommendations and technical presentations to different stakeholder levels |
Qualifications At A Glance
Google lists a clear baseline, and a few preferred areas that can set your profile apart.
Requirement Type | Details |
|---|---|
Education | Bachelor’s degree in Computer Science or a related technical field, or equivalent practical experience |
Core Experience | Building generative AI applications, intelligent agents or LLM-powered solutions and working directly with technical customers |
Language | Fluent English to support client relationship management |
Preferred | Agent frameworks, RAG architectures, multimodal solutions, enterprise AI platform governance, cloud networking |
Fluent English Is Mandatory
Agent Building Skills To Learn
If you want to strengthen your profile before applying, focus on how agents are actually orchestrated. Google specifically mentions Agent Development Kits and popular frameworks such as LangChain, LlamaIndex and AutoGen. Build at least one small project where an agent calls tools, keeps context across steps and handles failures gracefully, because troubleshooting agent behaviour is a daily part of this job.
Pay close attention to Retrieval-Augmented Generation. Understand how documents are chunked, embedded and retrieved, and how an LLM can be connected securely to Google Workspace or an internal database without exposing data it should not see.
Python LangChain LlamaIndex AutoGen RAG Vector Search
Multimodal AI Exposure
The preferred qualifications mention solutions that cover text, image, video and voice. Customers increasingly ask for assistants that can read a scanned invoice, summarise a recorded meeting or answer a spoken query. Experience with even one of these modalities in a personal or academic project will make your resume more interesting to a reviewer.
Security, Governance And Compliance
Deploying AI inside a large organisation is as much about trust as it is about capability. Expect to discuss access controls, data handling, governance policies and compliance requirements with customer security teams. Knowledge of serverless execution environments, virtualization, multi-tenant cloud infrastructure and secure cloud networking helps you answer these questions with confidence.
Customers rarely ask whether the model is smart. They ask whether it is safe to put in front of their employees.
Working With Customers
You will present to both engineers and business leaders, often in the same week. A deployment lead wants architecture detail, while an executive wants outcomes and timelines. Practise explaining the same solution at two levels, and you will find the role much easier.
Hybrid Discussion At Offer Stage Customer Facing
How The Hiring Process May Flow
Google notes that in most instances this role requires in-person interviews. A typical journey may look like the diagram below, although the exact steps can vary by team.
flowchart LR A[Application] --> B[Recruiter Screen] B --> C[Technical Rounds] C --> D[Customer Scenario Round] D --> E[Hiring Review]
Resume Tips For This Role
Lead with projects, not coursework. A short description of an LLM-powered application you built, what data it used and how you measured quality will carry more weight than a list of tools. Mention any customer-facing or presentation experience, since the role depends heavily on communication. Keep claims honest and specific, because technical interviewers will ask you to go deeper.
Keywords For Resume
Gemini Enterprise • Generative AI • LLM Applications • Agent Development Kit • LangChain • LlamaIndex • AutoGen • RAG • Multimodal AI • Google Cloud Platform • Serverless • Cloud Networking • Google Workspace Integration • AI Governance • Technical Advisory • Customer Success
Final Thoughts
This opportunity offers rare exposure to enterprise-scale generative AI adoption, where engineering depth and customer communication are valued equally. Candidates who can show real agent-building projects and explain their decisions clearly will be well placed to stand out.
The above article is written by me, a person interested in technology, automobiles, modern gadgets, movies, music, and clean aesthetics.
