DU Head (DPO)

Job Summary

Principal / Distinguished Architect – Google Cloud AI, Agentspace & Google AI Stack

Role Summary

We are seeking a very senior Google Cloud AI Architect to lead the design, delivery, and governance of enterprise-scale AI and Generative AI solutions on Google Cloud Platform (GCP). This role requires deep hands-on expertise across the Google AI stack, including Vertex AI and Agentspace, combined with strong customer-facing leadership and the ability to manage and mentor senior AI technical leaders embedded in customer environments.

The role operates at the intersection of AI platform strategy, cloud architecture, advanced GenAI engineering, and executive advisory, acting as a trusted advisor to customer CXOs while scaling high-performing AI architecture and engineering teams.

Key Responsibilities

Key Responsibilities

1. AI & Cloud Architecture Leadership

• Own end-to-end architecture for AI, GenAI, and data-driven platforms built on Google Cloud Platform.
• Define enterprise reference architectures, blueprints, and guardrails aligned with Google Cloud best practices.
• Lead architecture decisions for large-scale, mission-critical AI platforms across multiple customer programs.
• Act as final escalation point for complex architecture, scalability, security, and cost challenges.

2. Generative AI, Gemini & Agentspace

• Architect and govern GenAI solutions using:
  • Vertex AI (Gemini models, Model Garden, Extensions)
  • Google Agentspace for building enterprise-grade AI agents
  • Vertex AI Search & Conversation
• Design advanced GenAI patterns:
  • Retrieval-Augmented Generation (RAG)
  • Agentic workflows and multi-agent orchestration
  • Tool / function calling and enterprise system integration
• Define Responsible AI, safety, and governance frameworks aligned to Google AI principles.

3. Google AI & Data Platform Architecture

• Lead scalable AI and data platform architectures using:
  • BigQuery, BigLake, Cloud Storage
  • Dataflow, Dataproc, Pub/Sub
  • AlloyDB, Spanner (when applicable)
• Architect secure, multi-project GCP environments with strong network isolation and identity controls.
• Ensure high availability, resiliency, and performance for AI workloads.

4. MLOps, LLMOps & Engineering Excellence

• Define and enforce MLOps / LLMOps standards on GCP:
  • Vertex AI Pipelines and CI/CD
  • Model registry, versioning, and evaluation
  • Prompt lifecycle management and experimentation
• Establish best practices for:
  • Model quality, bias detection, and drift monitoring
  • LLM evaluation and hallucination mitigation
  • Cost governance and quota management

5. Security, Privacy & Responsible AI

• Architect secure AI systems with focus on:
  • IAM, service accounts, workload identity federation
  • VPC Service Controls, CMEK, data encryption
  • Secure GenAI patterns (prompt injection prevention, data isolation)
• Ensure compliance with enterprise and regulatory standards (GDPR, SOC2, ISO, HIPAA, etc.).

6. Customer & Executive Engagement

• Act as Chief / Principal AI Architect for strategic customer engagements.
• Lead architecture and AI strategy discussions with CIOs, CTOs, CDOs, and business leaders.
• Translate complex business challenges into scalable Google AI roadmaps.
• Support pre-sales and growth activities including:
  • Technical solutioning and deal shaping
  • Architecture due diligence and risk assessments
  • RFP responses and solution defenses

7. People & Technical Leadership

• Lead and mentor senior AI architects, principal engineers, and technical leaders embedded within customer environments.
• Establish architecture review boards and AI communities of practice.
• Drive consistency of architecture, coding standards, and AI governance across accounts.
• Coach leaders on executive communication, technical depth, and customer advisory skills.

Skill Requirements

Required Technical Expertise

Core Google Cloud Platform (Must-Have)

• GCP Architecture (Expert Level)
  • Multi-project design, shared VPCs, networking
  • Identity & security (IAM, IAP, VPC Service Controls)
  • Google Cloud architecture and reliability best practices
• Infrastructure as Code
  • Terraform, Deployment Manager

AI / ML & GenAI (Must-Have)

• Vertex AI
  • Gemini models, custom model training
  • Feature Store, Pipelines, Model Registry
• Agentspace
  • Agent design, orchestration, and tool integration
• Vector Search & RAG
  • Vertex AI Vector Search
  • BigQuery vector search

Data & Integration

• Data lakes and analytics (BigQuery, BigLake, Cloud Storage)
• Streaming and event-driven systems (Pub/Sub, Dataflow)
• API and microservices architectures (Apigee, Cloud Run, GKE)

DevOps, MLOps & Observability

• CI/CD pipelines (Cloud Build, GitHub Actions, Jenkins)
• Monitoring & observability (Cloud Monitoring, Cloud Logging)
• Model performance and GenAI observability frameworks

Experience & Qualifications

• 15+ years of overall technology experience.
• 8–10+ years in principal, enterprise, or chief architect roles.
• Proven experience delivering large-scale AI / GenAI solutions on Google Cloud.
• Strong background in customer-facing consulting or managed services delivery.
• Demonstrated experience managing senior architects and technical leaders.
• Prior role as Principal Architect, Distinguished Engineer, or Chief Architect preferred.

Leadership & Soft Skills

• Strong executive presence and storytelling ability.
• Ability to influence architecture and AI strategy at CXO level.
• Hands-on technical depth with strategic thinking.
• Passion for mentoring senior leaders and building AI talent pipelines.
• Comfortable operating in complex, multi-stakeholder enterprise environments.

Certifications (Highly Desirable)

• Google Cloud Certified – Professional Cloud Architect
• Google Cloud Certified – Professional Machine Learning Engineer
• Google Cloud Generative AI certifications
• TOGAF or equivalent architecture certification

Other Requirements

Required Technical Expertise

Core Google Cloud Platform (Must-Have)

• GCP Architecture (Expert Level)
  • Multi-project design, shared VPCs, networking
  • Identity & security (IAM, IAP, VPC Service Controls)
  • Google Cloud architecture and reliability best practices
• Infrastructure as Code
  • Terraform, Deployment Manager

AI / ML & GenAI (Must-Have)

• Vertex AI
  • Gemini models, custom model training
  • Feature Store, Pipelines, Model Registry
• Agentspace
  • Agent design, orchestration, and tool integration
• Vector Search & RAG
  • Vertex AI Vector Search
  • BigQuery vector search

Data & Integration

• Data lakes and analytics (BigQuery, BigLake, Cloud Storage)
• Streaming and event-driven systems (Pub/Sub, Dataflow)
• API and microservices architectures (Apigee, Cloud Run, GKE)

DevOps, MLOps & Observability

• CI/CD pipelines (Cloud Build, GitHub Actions, Jenkins)
• Monitoring & observability (Cloud Monitoring, Cloud Logging)
• Model performance and GenAI observability frameworks

Experience & Qualifications

• 15+ years of overall technology experience.
• 8–10+ years in principal, enterprise, or chief architect roles.
• Proven experience delivering large-scale AI / GenAI solutions on Google Cloud.
• Strong background in customer-facing consulting or managed services delivery.
• Demonstrated experience managing senior architects and technical leaders.
• Prior role as Principal Architect, Distinguished Engineer, or Chief Architect preferred.

Leadership & Soft Skills

• Strong executive presence and storytelling ability.
• Ability to influence architecture and AI strategy at CXO level.
• Hands-on technical depth with strategic thinking.
• Passion for mentoring senior leaders and building AI talent pipelines.
• Comfortable operating in complex, multi-stakeholder enterprise environments.

Certifications (Highly Desirable)

• Google Cloud Certified – Professional Cloud Architect
• Google Cloud Certified – Professional Machine Learning Engineer
• Google Cloud Generative AI certifications
• TOGAF or equivalent architecture certification