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AI / ML Researcher Available — 2 slots UTC+8

Ridhwan Amin

I build seismic inpainting models, anomaly detection systems, and hybrid RAG pipelines at UTP. I connect missing dots — using interpolation to find the best relation between sparse signals and hidden insights.

Seri Iskandar, Malaysia
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What I build

LLM Engineering

Designing, fine-tuning, and deploying large language models for real-world tasks. From prompt engineering and RAG pipelines to RLHF and LoRA fine-tuning on domain-specific corpora.

GPT-4o Claude Llama 3 LangChain RLHF LoRA

Generative AI

Building generative systems: diffusion models, VAEs, and multimodal pipelines. Research-grade experimentation combined with production-feasible architectures.

Diffusion Stable Diffusion VAE ControlNet Fine-tuning

Python Development

Fluent in the full Python ML stack. From async FastAPI services to GPU-accelerated PyTorch training loops, with clean, testable, production-ready code.

PyTorch FastAPI NumPy Pandas scikit-learn asyncio

Cloud Development

Deploying and scaling ML workloads on AWS and Azure. Containerised inference, managed vector stores, serverless pipelines, and IaC with Terraform.

AWS Azure Docker Kubernetes Terraform CI/CD
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Featured work

A moving deck of shipped products, healthcare platforms, and research infrastructure. Hover or focus the deck to inspect a card.

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Writing

All posts
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Frequently asked

Six to twelve weeks, embedded part-time with your team. I scope a concrete deliverable up front (a deployed RAG service with evals, an agent pipeline with observability, a training and serving stack with monitoring), then ship it. Open-ended retainers are possible but I prefer shape before commitment.
Evals first, vibes second. Before I touch a prompt or a model, I write a small labelled set and an eval harness that runs on every change. If I can't measure it, I won't pretend to be improving it.
Python + FastAPI on the backend. pgvector or Qdrant for retrieval. Modal or Cloudflare Workers for serving, depending on latency budget. PyTorch when I'm training, W&B for tracking. TypeScript + Astro on the web. I'm stack-pragmatic; I'll use what your team already runs if it gets the job done.
Yes. ML that doesn't reach users isn't really ML. I set up the CI, the canary rollout, the monitoring dashboards, the alerting. I'll hand the wheel back to your team with runbooks; I won't leave you a notebook and a prayer.
Contract-first, indefinitely. Open to interesting co-founding conversations if the problem is genuinely ML-shaped and the team has real domain context. Not looking for full-time employment right now.
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Let's work together

Whether you need ML infrastructure, a research collaboration, or just want to talk about seismic imaging, I'm reachable.

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