Lead GenAI Customer Engineer @ Google
Bridging Deep R&D, Corporate Strategy, and Production-Grade Engineering
I architect and prototype enterprise AI systems across distributed post-training, multimodal agents, and regulated workflows. I work across the full decision chain: frame the business and technical decision, build the critical path, demonstrate it under real constraints, and hand over something the customer team can operate.
My goal is not to choose between engineering and strategy. It is to use technical depth to improve consequential decisions—and to stay close enough to implementation to know when the slide deck is wrong.
- Build: Own the technically uncertain path from architecture to working proof.
- Decide: Connect model and platform choices to economics, security, governance, and organisational readiness.
- Multiply: Turn individual engagements into reusable architectures, public knowledge, and stronger delivery teams.
At Google, I advise APAC’s enterprise C-suites on the sovereign adoption of Generative AI, focusing on:
- Agentic Data Engines: Moving beyond simple RAG into autonomous, multi-modal reasoning loops.
- Infrastructure Economics: Optimizing elastic HPC and training/inference costs for the next billion tokens.
- Cognitive Trust: Engineering the guardrails, red-teaming frameworks, and safety layers required for enterprise-grade compliance.
- Next-Gen CX: Deploying hyper-personalized, real-time multimodal agents that redefine human-computer interaction.
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Under pressure: Management consulting taught me to remain useful when the room becomes difficult—turn conflict into decisions, convert ambiguity into an executable plan, and stay accountable through delivery rather than stopping at the presentation.
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Without a playbook: Research taught me to be comfortable with problems I have not solved before: form hypotheses, build experiments, and converge on a workable answer under real constraints.
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With ownership: I architect, prototype, build, demonstrate, and hand over. The work spans executive stakeholders, platform teams, researchers, and the engineers who must operate the result.
Bridging the gap between frontier research and production-grade systems.
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Architected distributed NeMo-RL clusters on GKE to fine-tune Gemma 3 using Group Relative Policy Optimization (GRPO). Optimized FSDP sharding on NVIDIA B200 clusters. Value: Achieved +6.2% absolute gain in MATH-500 accuracy via custom reward engineering. |
Architected a multi-modal Financial Audit engine using LMMs and advanced OCR. Automated the extraction of complex, unstructured data from heterogeneous financial instruments. Value: Eliminated 90% of manual data entry for Tier-1 financial institutions. |
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Developed high-fidelity Computer Vision pipelines for autonomous robotic systems. Focused on real-time object detection and kinematic path planning in dynamic environments. Value: Reduced manual oversight by 40% in industrial automation. |
Leveraging my background in Thermodynamics, I built Time Series Forecasting models for high-value industrial assets to predict failure modes before they occur. Value: Saved millions in unplanned downtime for energy providers. |
| Frontier GenAI & Research | Scalable Engineering & HPC | Strategy & Domain |
|---|---|---|
Gemini / Gemma 3 |
NVIDIA B200 |
C-Suite Advisory & Consulting |
GRPO & RLHF (NeMo-RL) |
Ray on GKE (KubeRay) |
Unit Economics of AI |
Vertex AI & Model Garden |
PyTorch FSDP & DCP |
AI ROI & TCO Modeling |
Agentic RAG / Agents |
vLLM & FlashInfer |
Sovereign AI Frameworks |
Dynamic Grounding |
Distributed Training (XLA) |
Gov & Risk Management |
"Science is only as useful as its ability to be democratized."
- Lead GenAI Solutions: Architecting AI roadmaps for APAC Decacorns, transforming legacy data into Agentic Intelligence.
- Open Source Authority: Co-author and PyPI maintainer of Amazon DenseClus, an AWS Labs package for mixed-type clustering with 138K+ downloads.
- 3M+ Professionals reached via technical publications and strategic guidance on AI infrastructure and economics.
- Global Footprint: Mechanical Engineering (🇨🇳) → Robotics Researcher at Tohoku (🇯🇵) → Statistical Physics PhD at NTU (🇸🇬) → Wolfram Summer School (🇺🇸) → CSIRO/McKinsey/AWS (🇦🇺) → Google.
Active contributor to the foundational frameworks defining the future of AI/ML.
