Machine Learning Engineer · Singapore

Engineering AI and data systems for real analytical work.

Machine Learning Engineer working across agentic AI, data engineering and applied machine learning, with a focus on turning complex analytical requirements into dependable systems built for real-world use.

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Capabilities

Working across the analytical system

Three connected areas, supported by validation, documentation, stakeholder communication and technical delivery.

01
AI Systems and Agentic Engineering
Designing request handling, analytical workflows, execution boundaries and feedback-led improvements for AI-assisted systems.
02
Data and Analytics Engineering
Placing data logic in the right layer, building scheduled workflows and shaping analytical products around practical use.
03
Applied Machine Learning and Research
Applying computational methods to text and geospatial questions while keeping methodology, attribution and limitations visible.

Featured work

Selected engineering problems

Three examples across agentic AI, data engineering and sustainability research.

ProfessionalAgentic AIJohnson & Johnson

Project 01

Architecting an analytical agent for governed data exploration

Natural-language analytical requests varied in intent and execution needs, making a single generation path difficult to control consistently.

Contribution Architected the Data Analyst agent, translating requests into SQL, Python and visualization workflows through intent-based routing, shared analytical definitions, query validation and containerised execution, then iterating with stakeholder feedback.

Engineering question How should interpretation, analytical logic and execution controls be separated instead of relying on one unconstrained prompt-to-answer step?

ProfessionalData engineering

Project 02

Re-architecting analytical computation for interactive performance

Recurring analytical logic was being recomputed during user requests, coupling heavy processing to an interactive reporting path.

Contribution Re-architected the workflow by moving repeatable computation into scheduled backend SQL processing and persisting monthly outputs for reporting, comparison and downstream reuse.

Engineering question Which calculations truly require request-time execution, and which can be computed earlier and owned by the data layer?

AcademicFinal Year ProjectApplied ML

Project 03

Personalized Movie Recommendation System

A solo final-year project comparing five recommendation approaches under the tension between personalization and discovery.

Contribution Built and compared an SVD baseline, SVD++, a PyTorch stacked autoencoder, a from-scratch Gaussian RBM and a weighted hybrid model, then validated the hybrid with a 10-person user study.

Research focus How can personalization be balanced with discovery, and can a weighted hybrid model capture the strengths of both a classical and a deep-learning approach?

View project case study Related education: BSc Computer Science, Machine Learning and AI

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Experience

Progressing from data foundations to AI systems

A concise role overview. Project pages will hold the technical depth.

2026 · Present

Machine Learning Engineer

Tata Consultancy Services · Johnson & Johnson

Focus Agentic analytics, applied AI systems and analytical workflow design.

Primary contribution Architected the Data Analyst agent within a broader multi-agent pilot, designing request interpretation, intent-based routing, shared analytical definitions, query validation and controlled SQL/Python execution.

Broader scope Worked across iterative agent refinement, analytical consistency, stakeholder feedback and evaluation, and internal technical tooling.

2024 · 2026

Data Engineer

Tata Consultancy Services · Johnson & Johnson

Focus Analytical platforms, backend computation and decision-support systems.

Primary contribution Re-architected recurring analytical computation into scheduled backend SQL workflows, persisted reusable outputs for downstream reporting, and engineered data and access logic supporting analytical products.

Broader scope Also worked across forecasting workflows, QA frameworks, reporting modernization, technical documentation and stakeholder-facing delivery.

Journey

Work is one part of the story.

Education, research, leadership and volunteering have shaped a path that extends beyond professional engineering.

Education

Leadership & Recognition

Leadership progression

SIM Information Technology Club

Progressed from subcommittee member to President of the SIM Information Technology Club, taking on broader responsibility for student leadership and club contribution.

Subcommittee: 2020–21
President: 2022–23

Beyond engineering

Formula 1

Inside a Formula 1 race weekend

Being selected to support a Formula 1 race weekend gave me a very different view of an event I had previously experienced only from the outside. Trackside, the scale comes from the people behind it: marshals, race officials, safety teams and operations working across the circuit.

A Formula 1 team working together trackside — Teams working across the circuit bring the race weekend together.

Formula 1 race control area — Race control provides the operational view of the circuit.

Formula 1 safety cars on the track — Safety teams are part of the visible rhythm of a race weekend.

Racing from trackside.

A closing view of the race weekend.

Selected credentials

Structured validation, applied in context

The Claude certification track, Scrum credentials and the cloud/ML coursework behind the work above.

Claude Certification Progression

Product & Delivery

Additional structured learning

View all learning

This is a shortlist. Google's ML/cloud specializations, earlier coursework and everything else with a verified certificate are on the full page.

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About

Interested in the decisions behind the system.

I started in data engineering, working closer to the calculations, workflows and reporting foundations behind analytical products. That experience shaped how I approach machine learning and AI today: not as isolated models, but as systems whose behaviour depends on the data, execution paths, validation and decisions around them.

I now work as a Machine Learning Engineer while pursuing a part-time MSc in Data Science for Sustainability at NUS. Across professional and academic work, I am most interested in problems that require both technical implementation and judgement: deciding where logic belongs, how a system should be evaluated, and what its outputs genuinely support.

Outside work and study, my interests have taken me from student leadership to volunteering inside a Formula 1 race weekend. I value opportunities that expose me to different people, disciplines and ways of working.

LinkedIn GitHub Resume Contact

Resume

A tailored résumé, on request.

The portfolio provides the broader picture. If you would like a concise résumé for a role, conversation or opportunity, I am happy to share one directly.

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Contact

Let’s discuss thoughtful engineering work.

Open to conversations about Machine Learning Engineering, AI Engineering, Agentic AI, Data Engineering and related technical work.

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