Enterprise Technology Architect

Overview

Enterprise Technology Architect role to architect complex end-to-end IT solutions across the enterprise, focused on Google Cloud Vertex AI model development and GenAI/agentic architecture.

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 first-party data (BigQuery, GCS, internal knowledge bases) and third-party 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

• Minimum 15 years of experience is required.
• 15 years of full-time education.
• Must have: Google Cloud Vertex AI Model Development.
• 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, and 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.
• Additional information: 15–18+ years overall, with 5+ years in AI / GenAI.

Nice to have

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

Details

• Location: Bengaluru.