Ankith Gunapal
California, United States
Summary
🤩
Rockstar🎓
Top SchoolAnkith Gunapal is a product-focused Deep Learning engineer based in California with 10+ years bridging software and hardware engineering for applied AI. He brings practical experience shipping computer vision and ML systems, complemented by contributions at Meta/PyTorch that include fine-tuning Llama Guard for multi-label privacy detection and streamlining conda nightly builds for PyTorch Serve. His academic background spans electronics and telecommunications to a Master of Information and Data Science from UC Berkeley, giving him a rare mix of signal-processing, systems, and data-science expertise. Ankith excels at turning research-grade models into production-ready workflows, with hands-on skills in model evaluation, deployment automation, and responsible-AI tooling. Colleagues rely on him to reduce operational friction—whether by improving CI/CD for ML packages or designing data pipelines that surface real-world model failures.
10 years of coding experience
Master of Information and Data Science, Master of Information and Data Science at University of California, BerkeleyMaster's degree, Telecommunications, Master's degree, Telecommunications at University of MarylandBE, Electronics & Communication, BE, Electronics & Communication at RV College Of Engineering
Github Skills (22)
transformers10
pytorch10
github-ci10
docker10
python10
llama10
machine-learning10
conda10
dockers10
cicd10
llm10
data-preprocessing10
dataprep10
ai10
fine-tuning10
Programming languages (8)
Github contributions (5)
pytorch/serve
Jul 2022 - Jan 2023
Serve, optimize and scale PyTorch models in production
Role in this project:
DevOps Engineer
DevOps EngineerContributions:2 releases, 837 reviews, 136 commits in 6 months
Contributions summary:Ankith primarily focused on automating the build and deployment processes for conda nightly binaries. They implemented and refined workflows for pushing conda nightly CPU binaries, merging multiple nightly runs, and addressing review comments to improve workflow efficiency. They also managed package dependencies, including installing and configuring miniconda, and integrating twine for package building and uploading. The user’s work streamlined the build, testing, and deployment of the project's conda packages.
cpupytorchpytorch-modelsservingin-production
meta-llama/llama-cookbook
Mar 2025 - Mar 2025
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama model family and using them on various provider services
Role in this project:ML Engineer
ML EngineerContributions:2 PRs in 15 days
Contributions summary:Ankith's commits primarily focus on fine-tuning and evaluating Llama Guard models within the context of responsible AI, specifically for detecting multiple privacy violations using the ai4privacy/pii-masking-65k dataset. They demonstrate the implementation of fine-tuning workflows, data preparation for multi-label classification, and assessment of model performance. The user's work directly contributes to enhancing the capabilities of Llama Guard in identifying and mitigating privacy risks within text data.
aifinetuninglangchainllamallama2
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