Chief AI Architect

ClifyX; San Francisco (CA); On-site; USD 260,000 - 380,000; Full time

Job summary

ClifyX is seeking a Chief AI Architect to define and drive the AI/GenAI architecture and industrialization roadmap across a large-scale Google Cloud portfolio.

This role turns AI from experimentation into production-grade capabilities, enabling agentic workflows, platformization, and measurable business outcomes across engagements. You will lead enterprise GenAI patterns, RAG usage, and secure, scalable solutions.

Qualifications

• 15–20+ years in AI/ML architecture or advanced engineering.
• Deep GenAI expertise: LLMs, RAG, agent frameworks.
• Strong track record with enterprise-grade AI systems.
• Experience with Google Cloud Vertex AI preferred.

Responsibilities

• Define end-to-end AI architecture vision and patterns.
• Lead design of enterprise GenAI applications and RAG patterns.
• Drive AI platformization and reusable accelerators across portfolios.
• Ensure scalable, secure, production-ready AI solutions.
• Advise CXOs and shape AI strategy in deals and engagements.
• Build AI capability roadmap and governance frameworks.

Skills

GenAI (LLMs, RAG, agents)

AI/ML architecture

Cloud AI ecosystems

Data platforms

Enterprise AI systems

Executive communication

Tools

Vertex AI

Google Cloud

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.

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:

Establish reference architectures and reusable patterns for:

Business priorities

Portfolio growth and differentiation

2. Agentic & GenAI Solution Design

Lead design of:

Enterprise GenAI applications

Define patterns for:

Retrieval-Augmented Generation (RAG)

Ensure solutions are:

Scalable

Secure

Production-ready

Drive AI platformization across the portfolio:

Shared services and APIs

Build accelerators for:

AI-led onboarding

Validation

Establish AI as a horizontal capability across all towers (FDE, ISV, GWS, etc.)

4. Data & AI Integration

Real-time and batch processing

Ensure tight integration between:

Bias and safety checks

Explainability

Ensure compliance with:

Security, privacy, and regulatory standards

Define guardrails for enterprise AI adoption

6. CXO Advisory & AI Evangelization

Act as the AI thought leader for client CXOs

Lead:

AI strategy discussions

Translate AI capabilities into:

Business outcomes

7. Deal Support & Technical Differentiation

Anchor the AI narrative in all strategic deals

Work with BRMs and CTO to:

Shape AI-led solutions

Support high-impact:

RFP s

Orals

Executive pitches

8. Talent & Capability Building

Define capability roadmap for:

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