Building AI-automated content pipelines — from raw concept to multi-channel video at scale
I'm an AI/ML Engineer specialising in end-to-end AI content automation. I design and ship production systems that turn a single concept into published video — orchestrating LLMs, image generators, video synthesis, and TTS into fully automated pipelines that run at scale.
My background spans AI/ML engineering (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 deep in AI video pipeline engineering — automated multi-channel content generation with Claude, Gemini, Kling, and Veo. The whole stack: storyboarding, image gen, video synthesis, audio, captions, and YouTube publish — no manual steps.
Focused on AI-automated video pipelines, GenAI workflows, and production ML systems. The featured project below is my main build right now.
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.
View on GitHubAI 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 production ML pipelines and LLM integration to cloud deployment and GenAI workflows.
Open to full-time AI/ML engineering roles from April 2026. I'd love to hear about opportunities in Singapore or remote.