AI / LLM Engineer — training, aligning & serving large language models in production.
Incoming MSc Artificial Intelligence, University of Edinburgh · Sep 2026.
I spent ~3 years at Otsuka Shokai (Japan) building LLM systems end-to-end — from post-training and alignment to multi-node inference serving and shipping the apps on top. I care about making models train faster, align better, and serve reliably at scale.
- 🔭 Now: deep open-source contributions to the LLM training/serving stack (vLLM, verl, SGLang, TRL).
- 🎓 Next: MSc AI @ Edinburgh — dissertation extending my RL-for-retrieval work.
- 🎯 Goal: Research / ML / Applied Engineer roles at frontier AI labs.
- 💬 Ask me about: RLHF pipelines (DPO/ORPO/PPO), distributed FSDP training, and high-throughput vLLM serving on H100/H200.
Post-training & alignment — SFT and preference optimization (DPO / ORPO / SimPO / PPO) on models up to 72B, using Axolotl & LLaMA-Factory. RL for agents — applied & benchmarked the s3 RL-search framework on a Japanese domain corpus; debugged the distributed FSDP → vLLM training stack to get it running (precision 47.5% vs 32.8% naive RAG, +14.7 pts on a 50-sample internal eval). Inference at scale — multi-node vLLM serving on H100 / H200; RAG pipelines with rerankers. Shipped products — solo-built an internal pre-visit assistant (MCP server + Databricks + LLM + React UI + CI/CD) and an AI counselor app, plus eval dashboards and data pipelines.
Post-training: TRL · Axolotl · LLaMA-Factory · verl | Serving: vLLM · SGLang | Infra: FSDP · Ray · Databricks
Open to research & applied ML engineering roles (UK, 2027).