I work at the point where doctoral AI research meets production engineering. My research applies long-document Transformer architectures (Longformer, LED) to the systematic evaluation of AI governance policy: models that read hundred-page policy documents whole, instead of losing the thread across chunked fragments.
On the engineering side, my day-to-day toolkit runs deep: reinforcement learning end to end (custom Gymnasium environments, PPO and SAC training with Stable-Baselines3, reward shaping and difficulty calibration against expert baselines), the Hugging Face ecosystem for transformer fine-tuning and inference, retrieval pipelines with sentence embeddings and extractive QA, and the full LLM training loop: RLHF and SFT workflows, preference ranking, rubric and scaffold design, adversarial testing. Everything I build ships as working, containerised software with measured results, not notebooks that die after the demo.
Through JAAI CreAtive World Ltd, I turn that stack into applied systems for businesses: agents, evaluation tools, and data products. Several are shown below as working examples.
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