Chief AI Architect

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.
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.
Key Responsibilities

1. AI Architecture & Strategy

Define the end-to-end AI architecture vision:
GenAI, ML, Data platforms, Agent frameworks
Establish reference architectures and reusable patterns for:
Vertex AI, LLMs, multi-model orchestration
Align AI strategy to:
Business priorities
Portfolio growth and differentiation

1. Agentic & GenAI Solution Design

Lead design of:
Agentic systems (multi-agent workflows, orchestration models)
Enterprise GenAI applications
Define patterns for:
Prompt engineering
Retrieval-Augmented Generation (RAG)
Tool augmentation / API integration
Ensure solutions are:
Scalable
Secure
Production-ready

1. AI Industrialization & Platforms

Drive AI platformization across the portfolio:
Reusable components
Shared services and APIs
Build accelerators for:
AI-led onboarding
Validation
Support workflows
Establish AI as a horizontal capability across all towers (FDE, ISV, GWS, etc.)

1. Data & AI Integration

Define architecture for:
Data pipelines
Feature stores
Real-time and batch processing
Ensure tight integration between:
Data platforms (BigQuery, Dataflow, etc.)
AI/ML models
Enable data-to-AI lifecycle maturity

1. Governance, Risk & Responsible AI

Establish AI governance frameworks:
Model evaluation
Bias and safety checks
Explainability
Ensure compliance with:
Security, privacy, and regulatory standards
Define guardrails for enterprise AI adoption

1. CXO Advisory & AI Evangelization

Act as the AI thought leader for client CXOs
Lead:
AI strategy discussions
Innovation workshops
Executive demos
Translate AI capabilities into:
Business outcomes
ROI-driven transformation cases

1. Deal Support & Technical Differentiation

Anchor the AI narrative in all strategic deals
Work with BRMs and CTO to:
Shape AI-led solutions
Position differentiated value propositions
Support high-impact:
RFP s
Orals
Executive pitches

1. Talent & Capability Building

Define capability roadmap for:
AI engineers
Data scientists
FDEs with AI specialization
Drive:
AI bootcamps
Certification pathways
Build a high-caliber AI engineering ecosystem
Required Qualifications
15–20+ years of experience in:
AI/ML architecture, data platforms, or advanced engineering roles
Deep expertise in:
GenAI (LLMs, RAG, agent frameworks)
Cloud AI ecosystems (preferably Google Cloud / Vertex AI)
Strong track record in:
Designing and deploying enterprise-grade AI systems
Preferred Qualifications
Experience in:
Agentic systems / autonomous workflows
AI platform engineering and MLOps
Exposure to:
Multi-cloud AI environments
Strong executive communication and thought leadership presence
Success Metrics (What Good Looks Like)
AI embedded across all major workflows and solutions
High adoption of AI accelerators and reusable components
Measurable business impact (cycle time reduction, cost savings, productivity gains)
Strong AI-led differentiation in deals and client engagements
Mature AI governance and production-grade implementations