I trained a LoRA on my own face. Here's every mistake.
Dataset curation, captioning, and rank/alpha sweeps on Flux — and why 18 photos beat 60.

AI engineer and founder of Umzobo. I fine-tune LLMs, diffusion models and LoRAs, build RAG systems, and deploy generation pipelines on serverless GPUs.
AI engineer working across generative AI, LLMs, VLMs, diffusion models, LoRA and computer vision. I fine-tune models, build RAG systems and ship production AI platforms — right now Umzobo, an AI media studio whose generation pipelines run on serverless GPUs.
Previously an AI Intern at LetsAI Solutions and a Generative AI Intern at Curlscape. B.Tech CSE (AI specialization) at Bennett University, CGPA 8.63. I like turning research into things people actually click on.
Built Umzobo end to end: an AI SaaS platform for media generation spanning video, image generation and editing, and creative workflows. Architected and deployed AI models and generation pipelines on serverless GPUs, with production auth, credits billing, custom LoRA support for client-facing use, and 50+ state-of-the-art models.
Helped develop, implement and fine-tune machine learning models, performed data analysis and preprocessing for training datasets, and worked with the team to optimise algorithms and improve model performance across AI/ML projects.
Researched, evaluated and fine-tuned large language models (LLMs), and curated and prepared datasets.
Fine-tune custom LoRA models for clients within Flux-based image generation systems, using Low-Rank Adaptation to deliver realistic, high-quality AI-generated images tailored to client needs.
An IDE you scroll through. Every repo opens with a live preview, and the full case study is one click away.
An end-to-end AI SaaS for media generation: video, image generation and editing, and creative workflows. Generation pipelines run on serverless GPUs, with production auth, credits billing, custom LoRA support and 50+ state-of-the-art models.
| Umzobo | GPU Fleet | MiniMax H3 | LTX 2.3 | LoRA · Flux | Medical ChatBot | CodeMind | Smart Exam | Eye Disease | Story KD | ViT · MNIST | EN→HI | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| LoRA / QLoRA | 0.6 | · | 0.6 | 0.6 | 0.9 | · | · | · | · | · | · | · |
| Diffusion | 0.6 | · | · | · | 0.9 | · | · | · | · | · | · | · |
| Serverless GPUs | 0.9 | 0.9 | 0.6 | 0.6 | · | · | · | · | · | · | · | · |
| ComfyUI | · | 0.6 | 0.6 | 0.6 | · | · | · | · | · | · | · | · |
| RAG | · | · | · | · | · | 0.9 | 0.6 | 0.9 | · | · | · | · |
| LangChain | · | · | · | · | · | 0.6 | · | · | · | · | · | · |
| LLMs | · | 0.9 | · | · | · | 0.9 | 0.6 | 0.6 | · | 0.9 | · | · |
| Transformers | · | · | · | · | · | · | · | · | · | · | 0.9 | 0.9 |
| Distillation | · | · | · | · | · | · | · | · | · | 0.6 | · | · |
| PyTorch | · | · | · | · | 0.6 | · | · | · | · | 0.6 | 0.6 | 0.6 |
| TensorFlow | · | · | · | · | · | · | · | · | 0.6 | · | · | · |
| Computer Vision | · | · | · | · | · | · | · | · | 0.9 | · | · | · |
Nine certifications, filed like a Finder folder. Click or tap one for Quick Look, then use ← → to flip through.
Dataset curation, captioning, and rank/alpha sweeps on Flux — and why 18 photos beat 60.
Retrieve, rerank, and refuse. A local vector index over PDFs, with an answer only when the pages support it.
Patches, heads, and a class token. A small ViT, written out, trained on digits.
Queues, GPU costs, and Bun on a small VPS. Notes from turning one idea into a film pipeline.
A small RAG agent grounded only in this site. Every answer cites where it came from, and it says so when it doesn't know.



















