Cloud AI Engineer, Technical Onboarding Center
Google (company site, opens in a new tab)3+ yrsPuneOn-siteDirect career pageEst. 25–45 LPA
Posted on 10 Sept
Reach people at Google
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Google you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Lead delivery, deployment, and implementation of Gemini Enterprise solutions using Agent Development Kits to resolve complex enterprise technical challenges.
- Serve as a trusted technical advisor to strategic customers to define AI strategies and accelerate Gemini Enterprise adoption.
- Provide architectural guidance, collaborate with engineering teams to address product gaps, and deliver tailored technical presentations to stakeholders including C-level executives.
Minimum Requirements
- Bachelor's degree in Computer Science or related technical field, or equivalent practical experience.
- Minimum 3 years of experience in software engineering, cloud architecture, or technical consulting, including 2 years building and deploying GenAI applications or LLM-powered solutions in production.
- Experience with Python and AI application architecture including API design, microservices, and integration of AI models in enterprise software.
- Preferred work locations: Hyderabad, Bengaluru, Gurugram, or Pune, India.
Ideal Candidate Profile
- Experienced in deploying and managing enterprise-grade generative AI platforms, with familiarity in AI security, LLM governance, and enterprise compliance.
- Skilled at leading technical project deployments and collaborating with engineering teams on large-scale cloud transformations.
- Strong background in scalable RAG architectures and integrating LLMs with external productivity tools and complex enterprise databases.