Join Deloitte's Technology & Transformation practice as a Manager | Full Stack Architect, focusing on Platform Development & Integration in Mumbai. You will drive the strategy, architecture, and transformation of enterprise-scale Agentic AI platforms. This role involves partnering with executive stakeholders to define AI platform vision, governance, and engineering standards for scaling AI capabilities across the organization.

Deloitte empowers companies to unlock the value within their data. Our practice offers strategic guidance and implementation services for business intelligence, data management, performance management, and cutting-edge analytics, including big data, cloud, cognitive, and machine learning technologies.

Lead the architecture and transformation of enterprise-scale Agentic AI platforms to support numerous use cases and development teams. Define reference architectures for various AI systems, including single-agent, multi-agent, and human-in-the-loop configurations. Oversee the design and implementation of core platform components such as orchestration, memory, knowledge management, tooling, governance, observability, and security.

Establish architecture standards across key frameworks like LangGraph, Google ADK, and Microsoft Agent Framework. Evaluate and select appropriate LLMs, foundation models, and agent frameworks for enterprise adoption. Develop and implement AI governance, risk management, and responsible AI frameworks to ensure secure and compliant operations. Drive the adoption of LLMOps, AgentOps, and robust evaluation practices.

Proven experience architecting enterprise-wide Agentic AI platforms supporting hundreds of use cases and development teams. Deep expertise in defining reference architectures for single-agent, multi-agent, and human-in-the-loop AI systems. Strong understanding of Agentic AI platform components including orchestration, memory, knowledge, tooling, governance, observability, and security layers. Experience designing enterprise RAG and GraphRAG architectures leveraging vector databases and knowledge graphs. Expertise in defining memory management strategies and establishing architecture standards across relevant frameworks. Experience evaluating and selecting LLMs and agent frameworks for enterprise adoption. Deep expertise in AI governance, guardrails, risk management, responsible AI, security controls, compliance, and auditability frameworks. Strong knowledge of LLMOps, AgentOps, evaluation frameworks, observability, and operational excellence practices. Experience designing scalable cloud-native AI platforms on Azure, AWS, and GCP. Strong enterprise integration experience across APIs, business applications, data platforms, and workflow engines. Ability to establish architecture review boards, engineering standards, and platform adoption roadmaps.

Preferred Qualifications: Bachelor's or Master's Degree in Computer Science, Engineering, Information Systems, or related field. Certifications in Azure, Google Cloud, AI Engineering, or Enterprise Architecture preferred. Proven consulting experience working with CXOs and business leaders. Experience across banking, insurance, manufacturing, or healthcare is highly desirable.