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
15 years full time education
