Kuala Lumpur, Malaysia
Head of AI, ML & Engineering · Entermind Malaysia
I build AI-first products, and the engineering organizations that ship them.
engineers led across AI/ML, backend, mobile & web in a 500+ employee company
delivery output at flat headcount via company-wide LLM automation
lift in AI brand citation scores (GEO platform, GXS · Grab Group)
loan-disbursal conversion uplift (BuddySmart agentic RAG)
cut in LLM API cost via model routing & caching (DailyTatva RAG v2)
reduction in lending default rates (credit-risk models)
Leadership
I lead AI and engineering at the level where a roadmap has to survive both a board conversation and a code review. Over 16+ years I've built and scaled engineering organizations from a handful of people to 35+ across AI/ML, backend, mobile and web, inside a 500+ employee company. I own engineering P&L (licenses, hiring, appraisals) and set the operating model that lets teams ship enterprise AI predictably: technical review gates, architecture sign-off, and delivery prioritization across concurrent engagements.
I carry that from strategy to production. At VerSe Innovation, India's largest short-video and news platform at 350M+ users, I owned the AI/ML roadmap across content and lending and turned it into business outcomes: a 25% loan-conversion uplift, 23% lower default rates, and 30–40% lower inference cost through model routing and cost governance. Today I run AI, ML & Engineering at Entermind, delivering enterprise and government AI across SEA and MENA, where I drove 3× delivery output at flat headcount and act as the primary technical partner to executives from a bank's Chief Credit Officer to the Grab Group.
I still design the systems myself: agentic RAG platforms, multimodal voice digital twins, and an open-source guardrail LLM. I care about the difference between a demo and a system that holds at scale, which comes down to unit economics, AI governance, evaluation before deployment, and the failure modes that only show up in production.
How I operate
I design engineering organizations and the technology portfolio behind them. That means scaling teams across AI/ML, backend, mobile and web, developing the people in them, and leading cross-functionally from strategy through delivery.
I own engineering P&L and treat AI product strategy as a commercial lever: supporting enterprise sales, enabling go-to-market, and building the strategic partnerships that turn capability into revenue.
I set the AI operating model (responsible-AI practice, governance gates, and enterprise adoption paths) and manage executive stakeholders from Chief Credit Officers to group leadership.
Expertise
Experience
Lead an 11-engineer team across backend, AI/ML, frontend and QA, and own the company-wide AI & engineering roadmap for enterprise and government clients across SEA and MENA.
De facto Head of Engineering for BuddyLoan; scaled the org from 20+ to 35+ engineers within a 500+ employee company.
Owned the AI/ML roadmap across content and lending for a 350M+ user platform while leading 20+ engineers.
Architecture across the Dailyhunt / Josh 350M+ user platforms.
Production ML for Dailyhunt / Josh.
iOS modernization and cross-platform delivery for Dailyhunt.
Enterprise and government mobility delivery across the GCC.
SmartTV, Xbox and Windows 8 platform applications.
Consumer apps for Jubilant Food and Religare Wellness.
Trained 100+ engineers across TCS fresher batches and IIIT-G cohorts.
Selected work
An always-on conversational AI colleague that replaces the intake form with adaptive follow-up questioning, surfacing real requirements and handing structured briefs to sales.
15% increase in solutioning lead calls
Fine-tuned Qwen2.5-32B to score LLM outputs against national policy principles and emit structured JSON compliance verdicts with safe rewrites. The firm's first open-source AI asset.
3K+ HuggingFace downloads
A multimodal conversational twin over enterprise knowledge with voice, memory, and agentic workflows, giving stakeholders 24/7 access to the Chief Credit Officer's expertise.
20% lower routine meeting load · 40% better info availability
Citation-aware analytics and optimization across four AI assistants with competitor benchmarking, targeting brand visibility inside AI-driven discovery.
45% lift in AI brand citation scores
A conversational loan-matchmaking assistant with real-time eligibility checks and personalized offer delivery. It became the platform's primary conversion lever.
25% disbursal conversion uplift · 20% retention gain
Personalized news retrieval at 350M+ user scale. Built the v1 retrieval/inference foundation, then added model routing and caching in v2 while improving retrieval quality.
30–40% cut in LLM API cost
Knowledge base
The proof of expertise is the content. Four bodies of work sit behind this page. Free and premium material live in the same lists, never partitioned off.
Focused deep-dives on one concept, like vector databases, agent memory, or LLM gateway architecture, plus interview scenarios worked end to end.
The methodology: how to approach designing any AI system. Requirements, evaluation harnesses, latency budgeting, failure modes, and when not to use an LLM.
Complete architectures for real problems like RAG chatbots, email agents, and multi-agent platforms, each walked through in the same fixed order.
Equivalent services across AWS, Azure, and GCP side by side: the hard limits, pricing shapes, gotchas, and a verdict for each component.
Featured
12 min read
18 min read
9 min read
Speaking & video
I break down AI engineering (RAG, agents, guardrails, and cost control) on my YouTube channel. My latest:
Contact
Open to VP / Head of AI & Engineering roles.
Also open to consulting, collaboration, or a talk. The fastest way to reach me is email.
