Subhadip Mitra
Data & AI @ Google Cloud • Other Affiliations - IIT Madras, BITS Pilani

At Google Cloud, I lead Data Analytics and serve as Site Lead for Southeast Asia, where I manage multi-million-dollar initiatives and mentor high-performing teams-all while remaining an active engineer and researcher. My background also includes founding a B2B digital marketplace, which has enriched my approach by blending startup agility with enterprise-grade strategy.
Core Expertise
- Leadership & Strategy: Shaping technology vision and guiding cross-functional teams.
- Hands-On Engineering: Developing innovative AI, Data, and Cloud solutions.
- Digital Transformation: Architecting scalable platforms that deliver measurable business impact.
- Entrepreneurial Insight: Merging startup innovation with proven enterprise methodologies.
Innovations & Research
- Multi-Agent Decision Framework - Invented an adaptive system for enterprise decision-making, (ARTEMIS, 2025)
- Next-Gen Data Platform Architecture - Framework reimagining data integration for the Generative AI era, (ETLC, 2024)
- Privacy & Consent Protocols - open frameworks (OLP, 2022; OConsent, 2021) for secure data sharing and transparent consent management.
- Proprietary Methods: Invented techniques for continuous context propagation and scalable workload discovery using billion-node graph models.
Current Research Interests
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Multi-agent Enterprise Systems - Building practical frameworks for coordinated AI agents that work together to solve complex business problems at scale, with emphasis on reliability and governance.
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LLM Infrastructure Optimization - Researching efficient deployment architectures, inference acceleration techniques, and cost-effective serving strategies for large language models in production environments.
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Contextual Representation for AI Systems - Improving how AI models maintain and process contextual information, with focus on efficient relationship modeling and information retrieval in practical applications.
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Spatiotemporal Graph Analytics - Applying graph theory to model how resources interact across both space and time, with practical applications in cloud infrastructure optimization and workload scheduling.
Connect
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