Data Architect

Project Role : Data Architect Project Role Description : Define the data requirements and structure for the application. Model and design the application data structure, storage and integration. 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, 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

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