# PyTorch developer — from training to serving, since 2024 > Model training, fine-tuning and LoRA/PEFT adaptation with PyTorch: where and what for I have used the framework since 2024. - Highlights: 2 years - Experience: Since 2024 - Updated: 2026-11-08 - Source: https://muhammetsafak.com/expertise/pytorch/ - Language: en-US - Author: Muhammet Şafak --- ## Me in the PyTorch ecosystem PyTorch is where I touch the model itself: training, fine-tuning and adaptation with LoRA/PEFT. The output of training does not end there: the road to putting the model behind an API is part of my [AI systems engineering](https://muhammetsafak.com/expertise/ai-systems/) work too. Two tools sit around the framework: Python as my main language and CUDA on the GPU side. TensorFlow is part of my model-training work too; I see the two as tools I choose by the job, not as opposites. This page describes the work I have done since 2024, not anything older. ## What is the PyTorch Developer role? PyTorch is an open-source deep learning framework used with Python: it provides the building blocks for tensor operations, automatic differentiation and the training loop. A PyTorch developer is the person who defines, trains and adapts a model with those building blocks. ## What does the PyTorch Developer do? I use PyTorch for model training, fine-tuning and adaptation with LoRA/PEFT. I run the training workload on the GPU with CUDA and move the trained model to a serving layer to put it behind an API. ## Technologies I pair it with - PyTorch: The deep learning framework I use for model training, fine-tuning and adaptation with LoRA/PEFT. - Python: The main language of my model-training, fine-tuning and data-preparation work. - CUDA: The NVIDIA platform I run training and inference workloads on the GPU with. - OpenAI Triton: The language and compiler I use to write custom GPU kernels for inference optimization. - NVIDIA Dynamo-Triton: The model serving server, formerly NVIDIA Triton Inference Server, that I use to put trained models behind an API. - TensorFlow: The deep learning framework I use in my model-training work. ## Frequently asked ### How long have you worked with PyTorch? Since 2024, which is 2 years. ### What does a PyTorch developer do? They define, train and adapt a model in PyTorch: prepare the data, build the training loop, evaluate the result. In my work that means model training, fine-tuning and adaptation with LoRA/PEFT. ### What is the difference between PyTorch and TensorFlow? Both are deep learning frameworks. I use both in my model-training work; this page covers PyTorch and the [TensorFlow page](https://muhammetsafak.com/expertise/tensorflow/) covers the other. I make no claim about which one is better; I think the work itself decides the choice. ### What are LoRA and PEFT, and why do you use them? PEFT (parameter-efficient fine-tuning) is the general name for methods that train a small set of parameters instead of updating every weight of a model; LoRA is one of them. I take this route when I adapt an existing model to my own work. ### Do you run PyTorch on the GPU? Yes. I run training and inference workloads on the GPU with NVIDIA's CUDA platform; the details are on the [CUDA page](https://muhammetsafak.com/expertise/cuda/). ### How do you serve a model you trained in PyTorch? I use NVIDIA Dynamo-Triton, formerly NVIDIA Triton Inference Server, to put trained models behind an API. When inference optimization needs custom GPU kernels, I write them with OpenAI Triton. ### Which language do you use PyTorch with? Python. It is the main language of my model-training, fine-tuning and data-preparation work; the [Python page](https://muhammetsafak.com/expertise/python/) covers the language itself.