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 Platform Architecture

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, plannerexecutor 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

Skilled 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 Graphbased 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
1518+ years overall, with 5+ years in AI / GenAI

Qualification 15 years full time education

📌 Enterprise Technology Architect (Bengaluru)
🏢 Accenture
📍 Bengaluru