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Note: This is a public version with certain details removed for privacy. For a comprehensive resume including specific project metrics and contact details, please reach out via email or LinkedIn.
Senior Engineering Leader with 15+ years building the teams, frameworks, and systems that turn Data and AI from research to production. Currently Head of Data & Analytics for Google Cloud in Southeast Asia - a practice built from zero, delivering enterprise Data and AI transformation across 7 countries.
Dual track as "Player-Coach": leading petabyte-scale data platforms and multi-agent systems for Fortune 500 clients, while driving innovation through published research (5 technical disclosures, 6 published packages on PyPI and Maven Central, plus open-source AI safety tools including sandbagging detection and activation steering). Member of Google Cloud delta, architecting solutions at the intersection of applied AI and enterprise scale.
Work Experience
Head of Data & Analytics, Southeast Asia | Site Lead, PSO Southeast Asia
Dual-track role combining technical innovation leadership with regional delivery management. Built Google Cloud's Data Analytics practice for Southeast Asia with delivery scope across JAPAC, while serving as Site Lead overseeing cross-practice operations in SEA. Member of delta - Google Cloud's innovation and transformation team architecting enterprise AI solutions at scale.
Strategic Leadership & Delivery
- Practice Leadership: Built Data Analytics practice for Southeast Asia from 0 to 1, recruiting and developing engineering talent while establishing the region's premier capability serving strategic enterprise clients across 6 countries.
- Regional Operations: Serve as Site Lead overseeing delivery governance across all 7 PSO practices (Data Analytics, AI/ML, Infrastructure, Security, Enterprise Architecture, Application Development, Delivery Management) in Southeast Asia, owning utilization and CSAT metrics (97%) while driving strategic pursuits and contributing to 100% annual revenue target attainment.
- Portfolio Management: Direct regional Data Analytics delivery portfolio across JAPAC while simultaneously overseeing cross-practice portfolio as Site Lead.
- Strategic Interventions: Led critical engagements for JAPAC strategic accounts including major financial services institutions, telcos and consumer electronics manufacturers, ensuring delivery excellence and client success.
- Sales & Product Alignment: Partner with Sales leadership on strategic pursuits and collaborate with Product Engineering to shape platform roadmap based on field insights and customer requirements.
- Escalation Management: Spearheaded cross-practice rescue operations for at-risk enterprise accounts with multi-million dollar project values, recovering strategic customers and converting potential platform exits into long-term partnerships.
- Enterprise Delivery: Delivered first-of-kind solutions including GenAI-powered reconciliation framework for a major airline (now replicated across JAPAC), large-scale ML platform migrations (30K+ notebooks), and petabyte-scale data platform modernizations.
- Executive Advisory: Partner with C-level stakeholders (CTOs, CDOs) to define data modernization and AI transformation roadmaps, translating technical capabilities into business outcomes.
- Agentic AI Transformation: Pioneered agentic AI adoption across all 7 PSO practices and 6 JAPAC sub-regions, building SDKs, agent catalog, and standardized templates while designing reusable governance frameworks that accelerated innovation and reduced delivery costs.
- Delivery Accelerators: Built agentic tool suites including architecture discovery (100M+ node graph modeling), automated data pipeline generation, and platform cleanup agents that recovered at-risk engagements and secured significant long-term cloud commitments.
- Data Strategy Practice: Built Data Strategy competency from 0, delivering 8-figure pursuit value across 14 strategic pitches in Asia Pacific while establishing critical data assets and new GTM offerings.
Technical Innovation & Research
- Published Research: 5 Google Technical Disclosures on AI and distributed systems - UPIR (automated system synthesis, 274x speedup), FTCS (context architecture for AI agents), ARTEMIS (multi-agent debate framework), ETLC (data processing for GenAI), and LLM inference optimization (speculative decoding, custom Triton kernels).
- CatchMe - Intelligent Trust Engine: Industry-agnostic agentic AI for enterprise trust decisions. APLS self-learning + cascade routing achieving 86% cost reduction, sub-50ms latency.
Principal Engineer - Data & Analytics Transformation
Led design and development of retail bank's data & analytics platform serving 11 markets, 100+ systems, and 1200+ users.
- Developed self-service ML Workbench reducing model deployment time from months to weeks
- Architected MarTech strategy driving 30% increase in customer acquisition through data-driven personalization
- Created credit risk models over 15,000+ named entities leveraging news trends and social signals, reducing potential losses by $5M
- Defined enterprise data strategy including third-party data governance, privacy frameworks, and cloud adoption roadmap
Principal Data Engineer / Solution Architect
Architected enterprise-scale data solutions for Fortune 500 clients across APAC.
- Designed 5 global data lakes with ETL pipelines handling 1.2 PB/hour and 40K daily files
- Engineered real-time platform processing 2.5M events/second, improving Ad campaign responsiveness by 80%
- Built ML fraud detection system achieving 60% fewer false positives and 25% higher detection rates, resulting in $3M savings
- Built and managed large-scale Hadoop clusters (300+ nodes) for banks and telcos across JAPAC
Software Engineering & Technical Leadership
Progressive advancement through software engineering, entrepreneurship, and technical leadership across systems development, marketplace platforms, and payments infrastructure.
- Microsoft (2010-2014): Windows Kernel development (Windows 7/8, Server 2012 R2), Azure ML implementations, CDN architecture optimization
- Truckaurbus (2014-2016): Founded B2B commercial vehicle marketplace - 15 cities, 25+ OEM/bank partnerships
- UTU Singapore (2016-2017): Led maiden Thailand technical development; bank integration; payment/rewards systems for merchants
Research & Open Source Engineering
Spark LLM Eval - Distributed Evaluation Framework
Distributed LLM evaluation framework built on Apache Spark for enterprise-scale model assessment. Addresses the gap in evaluating LLMs at scale with statistical rigor, integrating seamlessly with Databricks infrastructure.
- Distributed Processing: Pandas UDFs with Arrow for efficient batching, scales linearly across Spark executors for millions of examples.
- Statistical Rigor: Bootstrap confidence intervals, paired significance tests (t-tests, McNemar's, Wilcoxon signed-rank), and effect size calculations.
- Multi-Provider Support: Works with OpenAI, Anthropic Claude, Google Gemini, and vLLM with smart rate limiting (token bucket algorithms).
- Enterprise Integration: MLflow experiment tracking, Delta Lake versioning, and comprehensive metrics (lexical, semantic, LLM-as-judge).
LLM Inference Efficiency Research
Research implementations addressing the fundamental bottleneck in LLM inference: memory-bandwidth constraints rather than compute limits. Explores acceleration through speculative decoding, custom GPU kernels, and quantization strategies.
- Speculative Decoding Suite: Six techniques including standard speculation, tree speculation, EAGLE-style drafting, Medusa multi-head, KV-cache compression (8x compression via INT8/INT4 quantization + H2O eviction), and diffusion efficiency optimizations. Production systems report 2-3x inference speedup.
- Custom Triton Kernels: High-performance GPU kernels for transformer operations - RMSNorm (8.1x faster, 88% peak bandwidth), fused RMSNorm+Residual (6.0x speedup), SwiGLU (1.6x), INT8 GEMM (2x memory savings). Demonstrates memory-bandwidth optimization from 11% to 88% of A100 peak.
- Device-Agnostic Implementation: Supports CUDA, Apple Silicon (MPS), and CPU with full KV-cache integration reducing complexity from O(seq^2) to O(seq).
AI Metacognition Toolkit
Activation-level detection of sandbagging, deception, and situational awareness in LLMs. Linear probes achieve 90-96% accuracy across Mistral, Gemma, and Qwen models. Published on PyPI.
- Sandbagging Detection: Linear probes trained on activation differences detect sandbagging intent with 90-96% accuracy. Model-specific representations - no cross-model transfer.
- Steering Vectors: Activation steering reduces sandbagging behavior by 20% in Gemma models without retraining.
- Bayesian Situational Awareness: KL-divergence based detection of behavioral changes and "Observer Effects" during interaction.
Steering Vectors for Agent Behavior Control
Runtime control of LLM agent behaviors through activation steering vectors - modifying model outputs at inference time without retraining. Demonstrates more calibrated control than traditional prompting approaches with LangChain integration.
- Contrastive Activation Addition: Extract steering vectors from contrast pairs and inject into model activations for behavior modification.
- Uncertainty Calibration: Achieves 65% uncertainty detection on ambiguous questions while maintaining 100% confidence on factual ones - superior to prompting which causes indiscriminate hedging.
- Multi-Vector Composition: Dynamic strength adjustment per-request with interference mitigation for combining multiple behavioral controls.
- Production Ready: LangChain integration, tested on Mistral-7B, Gemma-2-9B, and Qwen3-8B models.
Education
Publications & Technical Disclosures
UPIR: Automated Synthesis and Verification of Distributed Systems
Framework combining formal verification, program synthesis, and machine learning to automatically generate verified distributed system implementations. Achieves 274x speedup with 60% latency reduction through compositional verification and proof caching.
ETLC: A Context-First Approach to Data Processing in the Generative AI Era
A comprehensive whitepaper introducing ETLC (Extract, Transform, Load, Contextualize), adding semantic, relational, operational, environmental, and behavioral context to data pipelines.
Field-Theoretic Context System (FTCS)
An innovative approach modeling context as interacting fields rather than discrete states, enabling natural context flow and dynamic evolution through partial differential equations.
ARTEMIS - Adaptive Multi-agent Debate Framework
Technical disclosure on an adaptive framework for multi-agent decision systems using structured debate protocols to enhance enterprise decision-making.
Data Monetization Strategy for Enterprises
A comprehensive framework for enterprises to transform data into economic value, establishing methodologies now implemented across multiple JAPAC organizations.
OConsent: Open Consent Protocol for Privacy and Consent Management with Blockchain
A blockchain-based protocol for transparent personal data processing, enhancing user control and compliance with data privacy regulations.
Skills & Technologies
Technology Leadership & Strategy
Data Engineering & Architecture
Generative AI & Machine Learning
Cloud Platforms & Infrastructure
Programming & Development
Notable Projects
LLMConsent
Privacy-preserving consent management protocol for LLM training data - enabling transparent opt-in/opt-out mechanisms with cryptographic verification.
- Decentralized consent registry on public blockchain
- Cryptographic proof of consent status
- Real-time opt-out enforcement for AI training
- GDPR-compliant privacy controls for GenAI era
Open Location Proof Protocol
A privacy-aware open protocol for non-repudiable location verification in physical or virtual spaces.
- Cryptographically secure yet privacy-preserving protocol
- Fully decentralized architecture resistant to tampering
- Published comprehensive specifications for industry adoption
OConsent - Open Consent Protocol
Production implementation of the OConsent research protocol - a working system for managing user consent and privacy on public blockchains.
- Full-stack implementation with live deployment at oconsent.io
- Smart contract suite for on-chain consent management
- GDPR-compliant with automated audit capabilities
SMPP Core
Modern Java 21 SMPP protocol implementation with virtual threads for high-performance SMS messaging.
- 1.8M PDU decodes/sec, 1.5M encodes/sec, 25K network round-trips/sec
- Complete SMPP 3.3, 3.4, 5.0 support with modular architecture
- Published on Maven Central
ISO8583 Simulator
High-performance financial message processing tool for ISO 8583, used by banks and payment processors.
- 180k+ TPS message parsing with Cython optimization
- Multi-network support (VISA, Mastercard, AMEX, Discover, JCB, UnionPay)
- Published on PyPI with AI-powered test generation