DU Head (DPO)
Noida, Uttar Pradesh
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 .
- Solid 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
📌 DU Head (DPO) (Noida)
🏢 HCLTech
📍 Noida