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Cloud AI Engineer, Technical Onboarding Center, Google Cloud

Google
Bengaluru, IND
Senior · 6+ years experience
Onsite
Discovered 1 weeks ago
python

Full Job Posting

Responsibilities

  • Lead and oversee the global delivery and implementation of Gemini Enterprise solutions, utilizing Agent Development Kits (ADKs) to solve highly complex, enterprise-scale technical challenges.
  • Serve as the trusted technical advisor to C-suite executives at Google’s most strategic global accounts, shaping their overarching AI strategy and accelerating the adoption of Gemini Enterprise.
  • Influence the core product roadmap by translating complex architectural challenges into actionable requirements for Google's engineering teams.
  • Deliver leading practice recommendations and high-stakes technical presentations to executive boards and key business stakeholders to secure massive-scale technical wins.
  • Act as a thought leader and mentor across the Google Cloud organization, elevating the technical acumen of engineers and shaping best practices for agentic AI architectures.

Minimum qualifications:

  • Bachelor's degree in Computer Science, or a related technical field, or equivalent practical experience.
  • 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 applications, intelligent agents, or LLM-powered solutions globally.
  • Experience in Python including implementation and deployment of tiered microservices applications.
  • Experience deploying production-grade cloud infrastructure or architecting and building cloud-based data solutions (e.g., enterprise data warehouses, data lakes, data pipelines, and associated technologies).

Preferred qualifications:

  • Master’s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
  • Experience pioneering multimodal AI solutions (text, image, video, and voice) to solve novel, previously unsolved enterprise workflow challenges.
  • Experience with AI security, Large Language Model (LLM) governance, enterprise guardrails, and global compliance requirements (e.g., data privacy regulations, data sovereignty).
  • Ability to architect highly resilient, globally distributed Retrieval-Augmented Generation (RAG) systems and securely connecting LLMs with disparate legacy enterprise databases.
  • Recognized industry thought leadership (e.g., patents, published research, keynotes) in Generative AI, agentic workflows, or large-scale cloud data infrastructure.

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