Chief Architect - Artificial Intelligence

Tata Consultancy Services; San Francisco (CA); Hybrid; USD 239,000 - 282,000; Full time

Job summary

Tata Consultancy Services seeks a Chief AI Architect to define and drive AI/GenAI architecture, solution strategy, and industrialization across a large Google Cloud portfolio. The role turns AI from experimentation into scalable, production-grade capabilities, enabling agentic workflows, platformized AI adoption, and measurable business outcomes across engagements.

The candidate will lead AI architecture, platformization, governance, and CXO advisory efforts, delivering ROI-driven

Qualifications

• 15–20+ years of experience in AI/ML architecture, data platforms, or advanced engineering roles.
• Deep expertise in GenAI (LLMs, RAG, agent frameworks) and cloud AI ecosystems.
• Strong track record designing and deploying enterprise-grade AI systems.

Responsibilities

• Define end-to-end AI architecture vision including GenAI, ML, data platforms, and agent frameworks.
• Establish reference architectures and reusable patterns for Vertex AI, LLMs, and multi-model orchestration.
• Lead design of agentic systems and enterprise GenAI applications; ensure scalable, secure, production-ready solutions.

Skills

GenAI

AI architecture

Cloud AI

Executive communication

Education

Bachelor of Science (B.Sc)

Tools

Vertex AI

Job description

Lead Design Of:

Agentic systems (multi-agent workflows, orchestration models)

Define Patterns For:

Prompt engineering

Job Description

Role: Chief Architect - Artificial Intelligence (Google Cloud Portfolio)

Location Options: Bay Area - California

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

• 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

• 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

• 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.)

• 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

• 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

• 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

• Deal Support & Technical Differentiation

Work With BRMs And CTO To:

Shape AI-led solutions

Position differentiated value propositions

Support High-impact:

RFP s

Orals

Executive pitches

• 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

Salary Range: $239,300 - $282,000 a year

Qualifications:

BACHELOR OF SCIENCE (B.Sc)