Aayush is available for hire
Hire AayushAayush Garg
Verified Expert in Engineering
Deep Learning Engineer and Developer
Location
Gurugram, Haryana, India
Toptal Member Since
August 4, 2023
Aayush is a seasoned full-stack deep learning engineer with a 7-year track record, renowned for transforming MVPs into scalable, real-world solutions for Toptal and Fortune 500 clients. Specializing in computer vision, NLP, and LLMs, he is adept in OpenAI's GPT-3.5/4, prompt engineering, as well as image/video generation using Stable Diffusion and GANs. Aayush"s proficiency in deploying models on AWS showcases his comprehensive expertise in AI solutions.
Portfolio
Toptal Client
Amazon Web Services (AWS), Artificial Intelligence (AI), OpenAI GPT-3 API...
FlixStock
Deep Learning, Hugging Face, PyTorch, NVIDIA TensorRT, Model Development...
Toptal Client
Python, Machine Learning, Data Science, Artificial Intelligence (AI)...
Experience
Availability
Part-time
Preferred Environment
Linux, Visual Studio Code (VS Code), Git, Python, MacOS, C
The most amazing...
...thing I developed is an AI-driven background generator capable of creating professional-looking backgrounds for products and people deployed on a client's site.
Work Experience
Senior Deep Learning Expert
2023 - 2024
Toptal Client
- Designed the optimal few-shot prompting system to leverage AI models, including LLaVA-1.6 and LLaMA-2, to create effective and precise prompts for Stable Diffusion, significantly enhancing the quality and relevance of generated images.
- Fine-tuned a Stable Diffusion 1.5 inpainting model on a custom proprietary dataset, optimizing the checkpoint for enhanced image editing and restoration capabilities.
- Optimized the deployment of Stable Diffusion image generation and LLM pipelines for cloud infrastructure, resulting in improved inference speed, reduced latency, and lower cloud deployment costs.
Technologies: Amazon Web Services (AWS), Artificial Intelligence (AI), OpenAI GPT-3 API, OpenAI GPT-4 API, Generative Pre-trained Transformers (GPT), Stable Diffusion, Python, Large Language Models (LLMs), Fine-tuning, AWS Deployment, Computer Vision, Natural Language Processing (NLP), NVIDIA TensorRT, PyTorch, OpenAI, LangChain
Software Development Engineer IV
2023 - 2024
FlixStock
- Led the development of fast, optimized LoRA and DreamBooth-based Stable Diffusion solutions for generating identities and products with varying aesthetics and poses.
- Spearheaded a team to develop an instant AI background generator that enables the creation of multiple professional-looking backgrounds for products and people.
- Developed a virtual try-on product to help customers visualize clothes, makeup, and accessories without putting them on physically.
- Deployed a custom stable diffusion image generation pipeline using ray-serve.
Technologies: Deep Learning, Hugging Face, PyTorch, NVIDIA TensorRT, Model Development, Model Deployment, Team Management, Leadership, Large Language Models (LLMs), Stable Diffusion, Computer Vision, Amazon Web Services (AWS), Google Cloud Platform (GCP), Text to Image, Product Design, ETL, LoRa, DreamBooth, Image Generation, Deep Neural Networks, OpenCV, Object Recognition, ChatGPT, OpenAI GPT-3 API, Prompt Engineering, OpenAI GPT-4 API, ray-serve, OpenAI
Generative AI Expert
2023 - 2023
Toptal Client
- Developed an optimized prompt engineering approach to generate high-quality product descriptions and metadata for home decor and furniture items, significantly enhancing search indexing and retrieval efficiency.
- Utilized advanced multimodal AI models, such as LLaVA-1.5/1.6 and GPT-4, to generate a comprehensive product metadata system, significantly enhancing search functionality.
- Architected an MVP hybrid search system by integrating OpenAI GPT models with Elasticsearch, enabling more accurate and context-aware results for the home decor and furniture inventory.
Technologies: Python, Machine Learning, Data Science, Artificial Intelligence (AI), Machine Learning Operations (MLOps), GPT, Amazon Web Services (AWS), OpenAI GPT-4 API, Modal, Prompt Engineering, Deployment, OpenAI, LangChain
Software Development Engineer III
2022 - 2023
Flixstock
- Devised and executed a state-of-the-art generative AI approach for creating realistic faces and identities.
- Pioneered a GANs-based generative AI solution to seamlessly generate human portraits in any desired pose.
- Developed an innovative image enhancement tool for eCommerce products.
- Created a cutting-edge image enhancement tool explicitly tailored for optimizing eCommerce product visuals.
Technologies: Deep Learning, PyTorch, Generative Adversarial Networks (GANs), Docker, Model Development, AWS Deployment, Team Management, Team Leadership, Product Design, Pandas, ETL, Image Processing, Stable Diffusion, Hugging Face, Artificial Intelligence (AI), Google Cloud Platform (GCP), Amazon Web Services (AWS), Machine Learning, Computer Vision, Diffusion Models, Large Language Models (LLMs), Text to Image, Deep Neural Networks, OpenCV, Object Recognition, ChatGPT, OpenAI GPT-3 API, Prompt Engineering, OpenAI GPT-4 API, ray-serve, OpenAI
Research Geophysicist
2019 - 2022
Shell
- Designed and implemented an innovative deep learning autoencoders-based approach for accelerated denoising of subsurface data.
- Engineered a real-time fault segmentation solution that significantly improved subsurface interpretation capabilities.
- Developed a cutting-edge deep learning (DL)-based Hessian operator to effectively mitigate crosstalk in large-scale multivariable optimization, specifically in solving wave equations.
- Conducted an industry-leading feasibility study on uncertainty quantification in subsurface realizations for carbon capture and storage, employing state-of-the-art DL-based approximate Monte Carlo techniques.
Technologies: C, Python, PyTorch, Physics, Geology, Image Retouching, Image Segmentation, Image Processing, Software Development, Stable Diffusion, Hugging Face, Artificial Intelligence (AI), Docker, Machine Learning, Computer Vision, Diffusion Models, Image Generation, Deep Neural Networks
PhD Researcher
2015 - 2019
TU Delft
- Developed an innovative deep learning (DL)-based super-resolution technique that successfully removes spatial aliasing from subsurface data, resulting in enhanced image quality and improved analysis accuracy.
- Researched reservoir-oriented joint migration inversion (JMI-res) technology, a groundbreaking solution that substantially reduces subsurface optimization costs while accurately estimating elastic parameters.
- Deployed the developed JMI-res technology in different energy companies' software infrastructures.
Technologies: C, Python, MATLAB, Optimization, Project Development, Artificial Intelligence (AI), Machine Learning, Computer Vision, Object Recognition
Experience
Spatial Aliasing Removal Using Deep Learning Super-resolution
https://github.com/garg-aayush/spatial-alias-removalDeveloped a cutting-edge deep learning solution that effectively eliminates aliasing, including discretization noise, from subsurface data in real-time. This product received widespread acclaim and recognition within the energy industry. Additionally, as part of a collaboration with the University of Amsterdam (UvA), I took the initiative to make the starter code and data publicly available through an open-source platform.
Interactive Finance Real-time Stock Price Tracker Dashboard
https://github.com/garg-aayush/finance_dashboard_exampleMade an interactive dashboard to track stock price movement with features to generate simple moving averages and candlestick pattern graphs for data analysis. The dashboard can track six companies' stock prices in real time.
Model Parallelism for Deep Learning Neural Networks Architectures
https://github.com/garg-aayush/model-parallelismIn the context of training large models, particularly for 3D image segmentation and reconstruction challenges, the issue of out-of-memory arises when the model's size exceeds the capacity of a single GPU. This project addresses this challenge by demonstrating the implementation of model parallelism, specifically for intricate networks featuring skip and residual connections, leveraging a pipeline mechanism to achieve efficient computation.
Deep Learning Templates for Efficient Experiment Tracking and Management
https://github.com/garg-aayush/pytorch-pl-hydra-templatesDeveloped various deep learning templates using the PyTorch Lightning framework and Hydra to facilitate efficient experiment tracking and project management. Additionally, created template scripts to enable comprehension and execution of training on distributed systems.
Education
2015 - 2019
PhD in High-performance Computing for Imaging
Delft University of Technology - Delft, Netherlands
2010 - 2015
Master's Degree in Physics
Indian Institute of Technology (IIT) - Roorkee, India
Skills
Languages
Python, Bash, C, Python 3, C++
Libraries/APIs
PyTorch, NumPy, SciPy, OpenCV, Pandas
Platforms
Linux, Docker, Google Cloud Platform (GCP), Amazon Web Services (AWS), Visual Studio Code (VS Code), MacOS
Other
Machine Learning, Deep Learning, Image Generation, Image Processing, Stable Diffusion, Hugging Face, Artificial Intelligence (AI), Computer Vision, Diffusion Models, Text to Image, Deep Neural Networks, Software Development, Team Leadership, Machine Learning Operations (MLOps), Generative Pre-trained Transformers (GPT), Large Language Models (LLMs), Object Recognition, OpenAI GPT-3 API, Prompt Engineering, OpenAI GPT-4 API, ray-serve, OpenAI, Physics, Geophysics, Algorithms, Simulations, Generative Adversarial Networks (GANs), Model Development, Team Management, Product Design, Geology, Image Retouching, Image Segmentation, Optimization, Project Development, Signal Analysis, Stock Market, NVIDIA TensorRT, Model Deployment, Leadership, LoRa, DreamBooth, Fine-tuning, Natural Language Processing (NLP), GPT, Modal, Deployment, LangChain
Tools
ChatGPT, MATLAB, Git, AWS Deployment, Plotly
Paradigms
ETL, Data Science
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