About

Hi, I’m Tanmay Dipak Patil — a Machine Learning Engineer with a focus on GPU programming, inference optimization, and diffusion research.

I currently work at ModelsLab, where I build and optimize inference interfaces for language, image, and audio models. I spend a lot of my time chasing lower latency, reading papers, and implementing ideas from scratch to go deep on diffusion research.

What I do

ML / AI: TensorFlow, PyTorch, Transformers, Diffusers, Computer Vision
GPU Programming: Triton, Cute-dsl, Hip, Hipkittens, CUDA
Programming: Python, JavaScript, Dart, C++
Tools: Git, Docker, Kubernetes, Django, FastAPI

Experience

ModelsLab — Machine Learning Engineer (Feb 2024 – Present)

Developed inference interfaces for language, image, and audio models, plus services such as realtime chat, voice cloning, and image/video synthesis & editing. Benchmarked approaches for arbitrary model serving in real time and made existing implementations much faster to reduce generation latency.
Trained and finetuned image generation models using LoRA, DPO, and RLHF, dedicating significant time to research and implementing ideas from scratch.
Managed GPU deployments and handled multiple major production outages.

PandasAI — Software Engineer Intern (Sep 2023 – Jan 2024)

Spearheaded development of open-source online connectors and streamlined pipeline construction for LLMs, enhancing data accessibility and deployment efficiency.

Intersense Technologies LLP — Python Developer Intern (Feb 2022 – Nov 2022)

Built an ML-powered automated offset correction unit for CNC machines on a Raspberry Pi 4, replacing manual offset correction using Python, PyQt5, and network programming.

Projects

Krea 2 Depth ControlNet LoRA — depth-conditioned image generation

Developed and published a depth-conditioned ControlNet-LoRA for Krea-2, enabling image-to-image generation that preserves 3D structure and composition from input depth maps.
Implemented latent-space depth control using Depth-Anything-V2, Qwen-Image VAE conditioning, and rank-64 LoRA adaptation, achieving 0.98–0.99 depth consistency across generations.

Enigma DSL — an MLIR-based GPU kernel compiler

Built a Python GPU kernel DSL that traces into a custom MLIR dialect and lowers to native GPU shading language, applying CuTe-style layout algebra (composition, coalesce, TV layouts) to map threads/wavefronts to memory.
Published to PyPI as enigma-dsl.

ModelQ — a lightweight, production-ready async task library

Simplifies development and execution of asynchronous tasks in distributed systems. Inspired by Celery, it provides a clear API for defining, scheduling, and managing background jobs and complex task workflows — handling millions of requests daily in production.

Education

CSMSS CSCOE — BTech in AI and Data Science, CGPA 8.84/10.0 (July 2021 – June 2024)

Achievements

Open source contributor to trending ML repos including Hugging Face Transformers, Hipkittens, and PandasAI.