LLM systems
RAG, LangGraph, LangChain, MCP, tool-calling agents, SEA-LION, prompt management, human-in-the-loop workflows.
I build AI systems for work that needs judgment.
7 years at Micron, building production systems for semiconductor operations. Now at AI Singapore (AIAP), building an AMD 100E industry AI solution. The standard: save time, show the evidence, and help people decide what to do next.
They bring in the right context, run the right tools, show the evidence, and turn AI output into drafts, analysis, decisions, or actions people can use.
The work spans the parts recruiters now screen for: LLM systems, retrieval, agents, evals, observability, ML models, APIs, data pipelines, and shipped user interfaces.
RAG, LangGraph, LangChain, MCP, tool-calling agents, SEA-LION, prompt management, human-in-the-loop workflows.
Evaluation harnesses, Langfuse, Arize Phoenix, MLflow, trace review, failure analysis, source grounding.
LightGBM, scikit-learn, PyTorch, TensorFlow, YOLOv8, OpenCV, MediaPipe, SHAP, feature engineering.
FastAPI, React, TypeScript, Dash, SQLAlchemy, PostgreSQL, SQLite, auth, rate limits, background jobs.
Docker, Railway, GitHub Actions, Playwright, APScheduler, GCP, AWS, Snowflake, Airflow, Kedro.
Workflow automation, semantic search, text-to-SQL patterns, root-cause investigation, stakeholder reviews, cross-site rollout.
Led AI-enabled transformation across four fabs (Singapore, Boise, Hiroshima, Taichung), with 6 direct + 9 indirect engineers and a $9M portfolio. Co-led low-power DRAM and HBM yield programs, described here without customer or internal programme details. Built ML risk-scoring that lifted yield 1–2% at production scale, a GenAI SOP workflow scaled to ~3,000 engineers, and a digital twin that cut chemical use 60%. ResNet-50 wafer-defect classifier (Innovation Award). Work contributed to $600M+ in aggregate program outcomes.
Industry AI solution through the AIAP 100E programme, powered by SEA-LION and production ML patterns. Agents, vision, sequence modelling, RAG, evals, MLOps.
I keep side projects practical: real URLs, real data where possible, validation gates, and clear boundaries when something is demo or research-only.
I Went from 64 to 100/100 on Smithery. Here's Every Fix.
MCP quality fixes: tool names, annotations, caching, Smithery config, and a reusable create-mcp skill.
Read ›PDF Parsing for Complaint Forms — Docling vs PyMuPDF vs PaddleOCR
Seven PDF parsers tested on scanned, handwritten, AcroForm, and degraded complaint forms.
Read ›I Built an AI Wine Deal Finder — Here's What 50 Bottles Taught Me
Wine price comparison for Singapore, with scraping, Vivino matching, and guarded daily refreshes.
Read ›Singapore-based, available from Q4 2026 for applied AI, agent workflow, and LLM product engineering roles.
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