Data Architect

Overview

Senior leadership role to own end-to-end agentic architecture for enterprise GenAI platforms on Google Cloud, designing multi-agent systems, RAG and Graph RAG pipelines, and governance for Vertex AI services.

What you'll do

• Own end-to-end agentic architecture for enterprise GenAI platforms using Google ADK and Vertex AI
• Define multi-agent and agent-of-agents architectures leveraging A2A (Agent-to-Agent) communication patterns
• Design and operationalize MCP-based orchestration layers for agent lifecycle management, coordination, and governance
• Establish reference architectures, design patterns, and reusable frameworks for scalable agent platforms
• Architect advanced RAG pipelines, including chunking strategies, embedding selection, retrieval tuning, and re-ranking
• Design and implement Graph RAG solutions using knowledge graphs for relationship-aware reasoning and contextual enrichment
• Lead Gemini Enterprise implementations, grounding GenAI models on 1P data stores (BigQuery, GCS, internal knowledge bases) and 3P data sources (SaaS systems, external content repositories)
• Define and implement agent observability and monitoring frameworks, including execution tracing, tool-call visibility, prompt and response auditing, and latency, cost, and quality metrics
• Own agent security architecture, covering secure tool access and permissions, policy-based agent behavior, and guardrails for autonomous actions
• Drive identity-aware GenAI architecture, including Workload Identity Federation, federated credentials, and secure access to enterprise systems, APIs, and data stores
• Govern adoption of Vertex AI Machine Learning services, including embeddings, model evaluation, pipelines, and inference
• Standardize prompt engineering and orchestration techniques, including role prompting, planner–executor patterns, and self-reflection loops
• Act as a strategic advisor to senior stakeholders on embedding agentic AI into enterprise processes
• Mentor architects and senior engineers and oversee delivery governance, scalability, and platform reliability

What you'll need

• Must have: Google Cloud Platform Architecture
• Minimum 15 years of experience
• Educational qualification: 15 years full time education
• Hands-on enterprise usage of Google Agent Development Kit (ADK)
• Agent-to-Agent (A2A) communication architectures
• MCP (Model / Multi-Agent Control Plane) or equivalent agent orchestration patterns
• Vertex AI Machine Learning services (training, prediction, embeddings, pipelines, evaluation)
• Gemini models and GenAI APIs on Google Cloud
• Deep expertise in RAG, chunking strategies, retrieval optimization
• Graph RAG / Knowledge Graph–based reasoning architectures
• Advanced prompt engineering, prompt chaining, and orchestration
• Experience building autonomous and semi-autonomous agents with tool calling
• 15–18+ years overall experience, with 5+ years in AI / GenAI

Nice to have

• Google Cloud certifications (Professional Google Machine Learning services / Cloud AI Engineer)
• BigQuery, GCS, Pub/Sub integration
• Enterprise data governance and AI safety frameworks

Details

• Location: Bengaluru
• Educational qualification: 15 years full time education