Subhadip Mitra
Architecting the Future of Data & Applied AI
Leading Google Cloud's Data & Analytics practice across Southeast Asia. Building teams, pioneering frameworks, delivering transformation at petabyte-scale.
Data & Analytics Manager | Site Lead Southeast Asia
Lead Data Analytics delivery and cross-practice operations across 7 countries in Southeast Asia. Member of delta - Google Cloud's innovation and transformation team architecting enterprise AI solutions at scale. Built 0-to-1 organization establishing the region's premier Data & Analytics practice. Pioneered technical frameworks (FTCS, ETLC, ARTEMIS, UPIR) and production-ready AI agent systems for autonomous data engineering. Led critical interventions safeguarding revenue while scaling enterprise Data and AI transformation across multiple sectors.
What I Do Best
- Technical Leadership - Building high-performing engineering teams
- Architecture - Petabyte-scale data platforms & ML systems
- Innovation - Research that becomes competitive advantage
- Transformation - Enterprise AI/data strategy & execution
Academic Background
- MBA, Business Analytics - BITS Pilani
- MTech, Software Systems - BITS Pilani
IIT Madras • IEEE • ACM • SCS • RIN
Recent Posts
The MCP Maturity Model: Evaluating Your Multi-Agent Context Strategy
It’s been nearly a year since Anthropic introduced the Model Context Protocol (MCP) in November...
UPIR: What If Distributed Systems Could Write (and Verify) Themselves?
The Data Platform Crisis Hiding Behind AI: Why you have 6 months to pivot
TL;DR: The Data Platform Crisis Hiding Behind the AI Revolution The Problem: Enterprise data platforms...
Speculative Decoding
LLM inference acceleration from first principles - speculative decoding, tree speculation, KV-cache compression, and diffusion efficiency with memory-bandwidth analysis
Technical Innovations
Automated Distributed Systems Synthesis
Revolutionary approach combining formal verification, program synthesis, and reinforcement learning to automatically generate verified implementations from specifications. Achieves 274x speedup for complex systems with 60% latency reduction.
Field-Theoretic Context System
Novel approach modeling context as interacting fields rather than discrete states, enabling natural context flow and dynamic evolution in AI systems.
Read Technical DisclosureContext-First Data Processing
Framework reimagining data integration for the GenAI era by adding semantic, relational, operational, and behavioral context to pipelines.
Read WhitepaperARTEMIS Multi-Agent Framework
Adaptive framework for multi-agent decision systems using structured debate protocols to enhance enterprise decision-making.
Read Technical DisclosureLLM Inference Efficiency Research
Reference implementations of acceleration techniques including speculative decoding, tree speculation, EAGLE, Medusa, KV-cache compression, and custom Triton kernels. Achieves 8.1x speedup with 88% peak bandwidth utilization on A100 GPUs.
CatchMe - Intelligent Trust Engine
Industry-agnostic agentic AI system for enterprise-scale trust decisions across Finance, Healthcare, Insurance, Cybersecurity, and Supply Chain. Features APLS (self-learning pattern synthesis) and five-level cascade routing, achieving 86% cost reduction with sub-50ms latency. Winner - Google Cloud PSO Hackathon JAPAC Regionals, qualified for World Finals.
Privacy & Consent Protocols
Open-source frameworks for secure data sharing and consent management. OLP & OConsent (2021-2022) focused on blockchain-based GDPR compliance. LLMConsent (2025) extends this to AI training data, agent permissions, and user sovereignty.
What I'm Exploring Now
Contextual Representation for AI
Advancing field-theoretic approaches to context processing in AI systems, focusing on efficient relationship modeling and dynamic evolution.
Multi-Agent Enterprise Systems
Building practical frameworks for coordinated AI agents solving complex business problems at scale with emphasis on reliability and governance.
Computational Efficiency for Generative AI
Exploring inference acceleration techniques for LLMs and diffusion models - speculative decoding, KV-cache optimization, kernel fusion, and hardware-aware algorithm design.
Orbital Edge Intelligence Systems
Advancing autonomous processing architectures for LEO satellite constellations - focusing on on-orbit analytics, inter-satellite task coordination, and distributed reinforcement learning for real-time geospatial intelligence without ground latency.
Let's Build Something Extraordinary
Looking for technical leadership, collaboration opportunities, or just want to discuss the future of AI? I'm always open to meaningful conversations.