Building production AI systems — governed RAG pipelines, LLM applications, and the interfaces that make them usable
I'm an AI/ML Engineer who ships production AI systems end to end. Most recently I designed and built a multilingual HR AI assistant single-handedly — a governed RAG pipeline with citation-bound answers and guards against invented figures, wired into enterprise messaging, an HRIS and Google Workspace, with an operations console so non-engineers can manage knowledge quality themselves.
My background spans AI/ML engineering (RAG and LLM applications, OT anomaly detection, GenAI localization, content automation) and computer science fundamentals (real-time systems, game engines, C/C++) — a rare combo that lets me build things that are both technically rigorous and actually ship. Whether it's a multi-channel YouTube engine cranking out 5+ videos a day or a cloud-deployed LLM workflow, I care about making AI work in production.
Right now I'm at OlaParty/Qoretex, owning an HR AI assistant as both product manager and sole engineer — retrieval architecture, FastAPI services, a Next.js operations console, Dockerised deployment, and the testing and observability to keep it honest. My AI video pipeline work — multi-channel generation with Claude, Gemini, Kling and Veo — is currently paused.
Production AI systems, GenAI workflows, and AI-automated video pipelines. My current build is the HR AI assistant at OlaParty/Qoretex; the video pipeline work below is paused.
A fully automated AI video production system. One pipeline takes a raw concept and produces a publish-ready YouTube video — no manual steps. The stack orchestrates Claude (scripting, storyboarding), DALL-E / Kling (image & video generation), Google Veo (video synthesis), and OpenAI TTS (narration) into a single FastAPI-driven workflow.
Runs 5 channels in parallel — horror, education, kids learning, AI creatures, and meme content — each with its own genre engine, beat scheduling, caption renderer, and age-tiered output. Auto-uploads to YouTube via OAuth, sends approval previews via Telegram, and tracks state across pipeline stages to recover from partial failures.
AI workflows for translation adaptation, dubbing, and short-form film/video localization at Lifegame. Built avatar-based ad generation pipelines and AI-assisted script creation from app store content using GCP Cloud Workflows.
End-to-end ML pipeline for OT cybersecurity: automated OpenSearch telemetry ingestion → time-series feature engineering → anomaly detection → LLM-powered analyst summaries. Streamlit dashboard for live investigation.
Multi-agent pipeline architecture where AI agents collaborate across roles — director, implementer, reviewer. Automated coding, content generation, and workflow execution via orchestrated Claude subagents and custom tooling.
A concise, ATS-friendly resume covering my end-to-end AI/ML engineering background — from a solo-built multilingual HR AI assistant and governed RAG pipelines to production ML, cloud deployment and GenAI workflows.
Open to full-time AI/ML engineering roles, available at 1 week notice. I'd love to hear about opportunities in Singapore or remote.