New opportunity

Cloud AI Engineer, Technical Onboarding Center, Google Cloud

Google · Bengaluru, India

About this role

Role Purpose: As a Cloud AI Engineer in the Technical Onboarding Center, you will lead the adoption of Gemini Enterprise and the broader Google Cloud Agentic AI suite for strategic customers. You will design production-ready architectures, transform customer workflows with autonomous agents, and serve as a technical advisor to C-suite executives. Responsibilities: Lead global delivery of Gemini Enterprise solutions using Agent Development Kits (ADKs); influence product roadmaps by translating architectural challenges into requirements; deliver high-stakes technical presentations to executive boards; and mentor internal teams on agentic AI best practices. Required Qualifications: Bachelor's degree in Computer Science or related field; 6 years of experience in software engineering, enterprise cloud architecture, or technical consulting; 3 years of experience in advanced AI/ML architecture, deploying generative AI, intelligent agents, or LLM-powered solutions; proficiency in Python and microservices; and experience deploying production-grade cloud infrastructure or data solutions. Preferred Qualifications: Master’s degree or PhD in AI/ML; experience with multimodal AI, AI security, LLM governance, and global compliance; ability to architect resilient, distributed RAG systems connecting to legacy databases; and recognized industry thought leadership in Generative AI or agentic workflows. Location: The role is based in India with options for Bengaluru, Gurgaon, or Hyderabad.

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Skills for this role

Gemini EnterpriseAgentic AIPythonCloud ArchitectureMicroservicesGenerative AILLMRAGMultimodal AIAI SecurityData PipelinesEnterprise Data Warehouses

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Architecting Production-Ready Agentic AI
The role requires designing and implementing robust, scalable architectures using Gemini Enterprise and Agent Development Kits (ADKs). You must demonstrate the ability to move beyond prototypes to deploy production-grade, autonomous agent workflows that integrate securely with enterprise data.
Strategic Technical Advisory for Executives
You will serve as a primary technical advisor to C-suite stakeholders. This requires translating complex AI capabilities into business value, delivering high-stakes presentations, and shaping the long-term AI adoption strategy for strategic global accounts.
Product Roadmap Influence
You are expected to act as a bridge between customer needs and internal engineering teams. By identifying recurring architectural challenges and technical barriers during customer onboarding, you will directly influence the evolution of Google Cloud's AI product roadmap.

Likely challenges to think through

Integrating LLMs with Legacy Enterprise Systems
A significant technical hurdle is architecting resilient RAG systems that securely connect modern LLMs to disparate, often rigid, legacy databases. Prepare by reviewing patterns for secure data retrieval, latency management, and maintaining data sovereignty in distributed environments.
Balancing Innovation with Enterprise Governance
Deploying agentic AI at scale requires navigating strict AI security, LLM governance, and global compliance requirements. You must be prepared to discuss how to implement guardrails that satisfy enterprise risk management without stifling the autonomous capabilities of the AI agents.

Practice your answers

Technical practice

Designing Resilient RAG Architectures

What to coverExplain how you would architect a globally distributed Retrieval-Augmented Generation system that securely connects to legacy enterprise databases. Focus on strategies for data synchronization, latency management, and maintaining data sovereignty while ensuring the LLM retrieves accurate, context-aware information.

Agentic Workflow Implementation

What to coverDescribe your approach to designing autonomous agent workflows using Agent Development Kits. Discuss how you handle task decomposition, error recovery in agent chains, and the evaluation of agent performance in production environments.

Multimodal AI Integration

What to coverHow do you incorporate multimodal inputs like video, audio, and images into enterprise AI solutions? Discuss the architectural considerations for processing these data types at scale and how they enhance business workflow automation.

LLM Governance and Security

What to coverWhat frameworks do you use to implement enterprise guardrails for LLMs? Address how you manage data privacy, prevent prompt injection, and ensure compliance with global data regulations when deploying generative AI solutions.

Microservices for AI Applications

What to coverDiscuss the role of microservices in deploying production-grade AI infrastructure. Explain how you manage service communication, scalability, and monitoring for complex, LLM-powered applications.

Translating Architectural Challenges to Product Requirements

What to coverProvide an example of how you have translated a complex customer architectural challenge into actionable product requirements. Explain your process for collaborating with engineering teams to influence product roadmaps.

Debugging Complex Agent Behaviors

What to coverWhat tools and methodologies do you use to monitor and troubleshoot non-deterministic agent behaviors? Focus on observability, logging, and performance tuning for LLM-based systems.

Data Pipeline Optimization

What to coverDescribe your experience building data pipelines for AI/ML workloads. Explain how you ensure data quality and freshness when feeding enterprise data warehouses into generative AI models.

Behavioural practice

Advising C-Suite Executives

What to coverDescribe a time you served as a technical advisor to executive stakeholders. How did you simplify complex technical AI strategies to align with their business objectives and secure their buy-in?

Leading Global Technical Delivery

What to coverShare an experience where you led the end-to-end delivery of a high-stakes technical solution for a strategic customer. How did you manage cross-functional teams and overcome technical barriers to ensure successful adoption?

Mentoring and Elevating Technical Acumen

What to coverProvide an example of how you have mentored internal teams on new technologies or best practices. How did you measure the impact of your mentorship on the team's overall technical capability?

Navigating Ambiguity in AI Projects

What to coverTell me about a time you faced a significant technical roadblock while implementing an AI solution. How did you identify the root cause and what steps did you take to resolve it while keeping the project on schedule?

Sharpen your knowledge

Topics to prepare

Agentic AI Architecture
Focus on the design patterns for autonomous agents that can reason, plan, and execute multi-step workflows. Review how to manage state, tool-use, and error handling in agentic systems. This is critical for the role's responsibility of transforming customer workflows using the Google Cloud Agentic AI suite.
Enterprise RAG and Data Integration
Study the architecture of resilient, globally distributed Retrieval-Augmented Generation (RAG) systems. Focus on securely connecting LLMs to disparate legacy databases and enterprise data warehouses. This addresses the preferred qualification regarding complex data integration for AI solutions.
AI Governance and Security
Review best practices for LLM governance, including enterprise guardrails, data privacy, and global compliance requirements. Understanding how to secure AI deployments is essential for advising C-suite executives on high-stakes implementations.

Skills to revise

Python Microservices Development
Practice building and deploying production-grade, tiered microservices using Python. Ensure you can demonstrate how these services interact with cloud infrastructure, as this is a core requirement for implementing Gemini Enterprise solutions.
Technical Executive Communication
Prepare to translate complex architectural challenges into high-level business value propositions. Practice delivering technical presentations to non-technical stakeholders, focusing on how AI strategy aligns with enterprise business goals.
Cloud Infrastructure Deployment
Gain hands-on experience with deploying production-grade cloud infrastructure. Focus on monitoring, debugging, and troubleshooting performance issues in large-scale AI applications, which is a key operational expectation for this role.

Your preparation checklist

Architect a resilient RAG system
Prepare a technical walkthrough of a production-grade RAG architecture that connects an LLM to legacy enterprise databases. Focus on how you handle data security, latency, and retrieval accuracy when integrating with disparate data sources.
Develop an agentic workflow case study
Create a detailed design document for an autonomous agent solution using Python. Explain how you would use an Agent Development Kit to automate a complex business workflow, including how you monitor performance and debug agent behaviors.
Prepare executive-level technical communication
Draft a summary of a complex AI implementation project tailored for a C-suite audience. Focus on translating technical architectural challenges into business value, risk mitigation, and strategic adoption roadmaps.
Review AI governance and compliance frameworks
Be prepared to discuss your approach to LLM governance, including data privacy, sovereignty, and enterprise guardrails. Research how these requirements apply to global deployments in regulated industries.
Formulate strategic questions for the interviewers
Prepare thoughtful questions regarding the current challenges strategic customers face when adopting Gemini Enterprise, how the Technical Onboarding Center influences the product roadmap, and how the team balances innovation with enterprise-grade reliability.

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