Job Description
We are seeking a Chief AI Architect to define and drive the AI/GenAI architecture, solution strategy, and industrialization roadmap across a large-scale Google Cloud portfolio.
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This role is responsible for turning AI from experimentation into scalable, production-grade capabilities, enabling agentic workflows, platformized AI adoption, and measurable business outcomes across all engagements.
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Key Responsibilities
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1. AI Architecture & Strategy
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Define the end-to-end AI architecture vision:
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GenAI, ML, Data platforms, Agent frameworks
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Establish reference architectures and reusable patterns for:
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Vertex AI, LLMs, multi-model orchestration
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Align AI strategy to:
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Business priorities
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Portfolio growth and differentiation
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2. Agentic & GenAI Solution Design
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Lead design of:
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Agentic systems (multi-agent workflows, orchestration models)
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Enterprise GenAI applications
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Define patterns for:
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Prompt engineering
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Retrieval-Augmented Generation (RAG)
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Tool augmentation / API integration
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Ensure solutions are:
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Scalable
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Secure
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Production-ready
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3. AI Industrialization & Platforms
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Drive AI platformization across the portfolio:
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Reusable components
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Shared services and APIs
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Build accelerators for:
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AI-led onboarding
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Validation
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Support workflows
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Establish AI as a horizontal capability across all towers (FDE, ISV, GWS, etc.)
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4. Data & AI Integration
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Define architecture for:
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Data pipelines
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Feature stores
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Real-time and batch processing
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Ensure tight integration between:
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Data platforms (BigQuery, Dataflow, etc.)
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AI/ML models
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Enable data-to-AI lifecycle maturity
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5. Governance, Risk & Responsible AI
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Establish AI governance frameworks:
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Model evaluation
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Bias and safety checks
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Explainability
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Ensure compliance with:
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Security, privacy, and regulatory standards
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Define guardrails for enterprise AI adoption
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6. CXO Advisory & AI Evangelization
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Act as the AI thought leader for client CXOs
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Lead:
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AI strategy discussions
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Innovation workshops
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Executive demos
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Translate AI capabilities into:
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Business outcomes
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ROI-driven transformation cases
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7. Deal Support & Technical Differentiation
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Anchor the AI narrative in all strategic deals
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Work with BRMs and CTO to:
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Shape AI-led solutions
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Position differentiated value propositions
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Support high-impact:
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RFP s
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Orals
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Executive pitches
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8. Talent & Capability Building
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Define capability roadmap for:
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AI engineers
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Data scientists
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FDEs with AI specialization
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Drive:
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AI bootcamps
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Certification pathways
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Build a high-caliber AI engineering ecosystem
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Required Qualifications
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15–20+ years of experience in:
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AI/ML architecture, data platforms, or advanced engineering roles
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Deep expertise in:
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GenAI (LLMs, RAG, agent frameworks)
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Cloud AI ecosystems (preferably Google Cloud / Vertex AI)
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Strong track record in:
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Designing and deploying enterprise-grade AI systems
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Preferred Qualifications
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Experience in:
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Agentic systems / autonomous workflows
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AI platform engineering and MLOps
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Exposure to:
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Multi-cloud AI environments
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Strong executive communication and thought leadership presence
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Success Metrics (What Good Looks Like)
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AI embedded across all major workflows and solutions
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High adoption of AI accelerators and reusable components
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Measurable business impact (cycle time reduction, cost savings, productivity gains)
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Strong AI-led differentiation in deals and client engagements
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Mature AI governance and production-grade implementations