AI Engineer

Production LLM Systems & Evaluation

Python / C++

Contact

I build

AI systems

that earn

trust

in production

I build LLM systems end to end on on-prem GPUs: ingestion, retrieval, evaluation, serving, and the tools engineers open every day. Every number in an answer points back to its source cell, and every change is measured against a gold set before it ships.

Core focus

LLM Systems
Retrieval & Ranking
LLM Evaluation
GPU Inference
Guardrails
Backend Engineering

From ingestion
to reliable answers

Experience

Cooler Master logo

Cooler MasterCurrent

AI Engineer

Singapore | Jul 2026 - Present

Build the company's on-prem LLM systems end to end, as the sole engineer on most of them: data ingestion, retrieval, evaluation, GPU serving and deployment. Own 5 production systems and 2 HR pilots four and a half months in, and screened 37 AI proposals from 13 departments.

  • Lab report Q&A

    Text-to-SQL and hybrid RAG over 3,500+ lab reports, every number cited to its source cell. The lab has asked 470 questions in 15 weeks.

  • Evaluation that gates releases

    Replayed 69 real user questions and fixed the 23 bad answers; every change passes a 264-case gold set. A 7B tied 32B and 72B at 86-87%, so it shipped at 3x lower latency.

  • GPU inference

    Served LLM, VLM and streaming speech models with vLLM on an NVIDIA DGX B200. Rebuilt live captions on Qwen3-ASR, term accuracy 58% to 86%, and moved 4 apps to a new model gateway in 4 days.

  • Silent failures, caught

    Found a vision model misreading tables without warning and made two reads agree before a value is stored, then corrected 46 stored tables. Caught context overflows on the new model gateway that were silently switching JSON mode off.

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AdvancedGRC

Software Engineer Intern (AI/ML Systems)

New York City | May 2025 - Apr 2026

Built Osmos solo during a 12-month placement: a self-hostable RAG platform for PDF-heavy support documents, covering ingestion, retrieval, reranking, evaluation and the product surfaces on a FastAPI backend.

  • Support tickets down 36%

    Shipped the RAG platform (FastAPI, Chroma, E5, vLLM) for daily internal support work.

  • Judged pass rate 67% to 98%

    Added a Hugging Face cross-encoder reranker and answer/abstain gating, so the system declines instead of guessing.

  • Benchmarks, not demos

    Warm latency, ingest throughput, concurrency and retrieval ablations; 132 ms p50 warm latency.

  • Research time cut 60-70%

    Automated extraction across all 50 US state insurance regulator sites in Python.

DigiPen Institute of Technology Singapore logo

DigiPen Institute of Technology Singapore

Teaching Assistant

Singapore | Aug 2023 - Dec 2024

Supported 8 computer science and math modules with more than 200 students: led labs, debugging sessions and office hours, and graded against clear, consistent standards.

DT Asia Pte Ltd logo

DT Asia Pte Ltd

Software Engineer

Singapore | Jul 2019 - Jul 2020

Promoted from intern. Built Syslog-ng log pipelines into Azure and AWS CloudWatch, saving $50K+ a year, hardened SSH remote-access infrastructure handling 100K+ sessions a month, and ran live product demos for customers.

Projects

Tech stack

  • LLM systems

    • RAG
    • Text-to-SQL
    • LLM evaluation
    • JSON-schema output
    • Citation guardrails
    • VLM OCR
  • Retrieval and ranking

    • BM25 + dense hybrid
    • BGE-M3
    • E5
    • Cross-encoder reranking
    • Learning-to-rank
    • OpenSearch
    • Chroma
  • Models and inference

    • PyTorch
    • Hugging Face Transformers
    • vLLM
    • LiteLLM
    • LightGBM
    • Qwen LLM, VLM and ASR
    • NVIDIA DGX B200
  • Backend and infra

    • FastAPI
    • WebSockets
    • SQLite
    • Docker Compose
    • nginx
    • Linux
    • Git
    • pytest
  • Languages and graphics

    • Python
    • SQL
    • C++
    • C
    • C#
    • JavaScript
    • Bash
    • GLSL
    • OpenGL

Contact

Let's
talk.

I build production LLM systems, from retrieval and evaluation to on-prem GPU serving. Always happy to hear about interesting AI engineering work or to compare notes on LLM systems and retrieval.