GCP Architect - Global head - Leadership

Job Title: Senior Technical GCP Delivery Head (Agentic AI & Data)

Exp: 10yrs to 14yrs

Location: PAN INDIA

Core Mission: To lead the architectural design and delivery of GCP-native platforms where Data is the

Foundation and Agentic AI is the Engine.

1. Role Executive Summary

As the Senior Technical Delivery Head, you are the visionary lead for our most complex Google Cloud

engagements. You will move beyond cloud migration to intelligent transformation. Your primary

responsibility is to architect and deliver Agentic Workflowsautonomous AI systems that can reason,

use tools, and access real-time data to solve business problems. You will bridge the gap between Data

Engineering and Generative AI to create scalable, production-grade agentic ecosystems.

2. Key Responsibilities (The Agentic & Data Core)

Agentic Orchestration & Delivery: Lead the deployment of multi-agent systems using Vertex AI

Agents and LangGraph. You will be responsible for the technical delivery of agents that don't just

chat, but execute complex tasks (e.g., automated procurement, intelligent supply chain

rerouting).

Data-Centric AI Strategy: Oversee the architecture of BigQuery-centric Data Clean Rooms and

Data Fabrics (using Dataplex) to ensure AI models are grounded in high-quality, real-time

enterprise data.

RAG & Vector Architecture: Direct the implementation of advanced Retrieval-Augmented

Generation (RAG) patterns, utilizing Vertex AI Vector Search and AlloyDB for high-performance

context retrieval.

LLMOps & Governance: Establish the Factory Floor for AI delivery, including automated model

evaluation, prompt versioning, and safety guardrails using GCP Model Armor and Sensitive Data

Protection.

Technical Governance: Conduct deep-dive architectural reviews to ensure every project adheres

to the Google Cloud Well-Architected Framework, with a specific focus on the AI/ML Pillar.

3. Technical Stack Requirements

Pillar Focus Areas

Agentic AI Vertex AI Agent Builder, LangChain, Function Calling, Reasoning Engine, Model Garden

(Gemini 1.5 Pro/Flash).

Data

Engineering

BigQuery (BigLake, Omni), Dataflow (Streaming), Pub/Sub, Dataplex for Data

Governance.

Vector &

Search Vertex AI Search and Conversation, Vector Search, pgvector on Cloud SQL/AlloyDB.

Cloud Native GKE (for hosting custom agent services), Cloud Run, Terraform (Infrastructure as Code).

AI Safety Vertex AI Model Monitoring, Data Masking, VPC Service Controls for AI workloads.

4. Qualifications

Experience: 12+ years in Technical Delivery/Architecture, with at least 4 years focused on

Data/AI at scale on GCP.

Architectural Depth: Proven track record of moving AI models from PoC (Proof of Concept) to

Production with full CI/CD and LLMOps pipelines.

Leadership: Experience managing a Technical Office of 20+ Lead Architects and Data

Engineers.

Education: Master's in CS, AI, or Data Science preferred.

Certifications:

o Required: GCP Professional Cloud Architect.

o Preferred: GCP Professional Data Engineer OR Professional Machine Learning Engineer.

5. Success Metrics (KPIs)

1. Agent Autonomy Score: Success rate of deployed agents in completing multi-step business

processes without human intervention.

2. Time-to-Insight: Reducing the latency between raw data ingestion in BigQuery and its availability

for AI agent grounding.

3. Inference Unit Economics: Optimizing token usage and model selection (e.g., Gemini Flash vs.

Pro) to maintain project margins.

4. Architecture Reusability: Creation of modular Agentic Blueprints that can be deployed across

multiple clients.