Enterprise Technology Architect

Project Role : Enterprise Technology Architect

Project Role Description : Architect complex end-to-end IT solutions across the enterprise. Apply the latest technology and industry expertise to create better products and experiences.

Must have skills : Google Cloud Vertex AI Model Development

Good to have skills : NA

Minimum 15 Year(s) Of Experience Is Required

Educational Qualification : 15 years full time education

Summary

We are seeking a Senior Manager – GenAI & Agentic Architecture with deep expertise in Google Agent Development Kit (ADK), Agent-to-Agent (A2A) and MCP-based orchestration, and Vertex AI Machine Learning services.

This role is responsible for designing, scaling, and governing enterprise-grade multi-agent systems that leverage Generative AI, RAG, Graph RAG, and Google foundation models.

The ideal candidate is a visionary architect who can bridge business workflows, data platforms, and autonomous AI agents at scale.

Roles & Responsibilities

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 optimal 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 (1P) enterprise data stores such as BigQuery, GCS, and internal knowledge bases, as well as third-party (3P) data sources such as SaaS systems and external content repositories

Define and implement agent observability and monitoring frameworks, including agent 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, with strong understanding of 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 oversee delivery governance, scalability, and platform reliability

Professional & Technical Skills

Must Have Skills

Google Agent Development Kit (ADK) – hands-on enterprise usage

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

Good 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

Additional Information:

15–18+ years overall, with 5+ years in AI / GenAI