Shubham Anilkumar Jain, Developer in London, United Kingdom
Shubham is available for hire
Hire Shubham

Shubham Anilkumar Jain

Verified Expert  in Engineering

Artificial Intelligence Developer

London, United Kingdom
Toptal Member Since
April 14, 2022

Shubham is a Ph.D. student and research assistant in the Computational Privacy Group at Imperial College London. He is an expert in deep learning, computer vision, security, privacy in machine learning, and privacy-preserving analytics. Shubham has also worked extensively as a full-stack engineer, developing a wide range of applications, from a prototype for a startup to a large-scale system intended for nationwide deployments.


Imperial College London
Data Privacy, Node.js, Analytics, Python, Django, Deep Learning...
Budgie Health Inc.
Python, Jupyter Notebook, Data Engineering, ETL, Jupyter
Ribbon Home, Inc.
Data Science, Data Analysis, Statistical Analysis, Model Development...




Preferred Environment

Python, Visual Studio Code (VS Code), Slack, PyTorch, Django, NumPy, Scikit-image, Scikit-learn, Deep Learning, Deep Reinforcement Learning

The most amazing...

...thing I've developed is a large-scale privacy-preserving analytics system for developing countries. I led the project and deployed it in Senegal and Colombia.

Work Experience

Research Assistant

2018 - 2023
Imperial College London
  • Led and developed a large-scale privacy-preserving analytics system for telecommunication companies in developing countries. The architecture was published at a leading conference, and the system was deployed in Senegal and Colombia.
  • Built a demonstration with Raspberry Pi, React, and Python to show the security risks of WiFi networks. The demo has been displayed to several luminaries and is used at Imperial's Executive MBA.
  • Created a system to test the robustness of specific machine learning models against adversaries trying to fool the system. Developed more robust models as a defense.
Technologies: Data Privacy, Node.js, Analytics, Python, Django, Deep Learning, Computer Vision, Data Science, Jupyter Notebook, Image Processing, TensorFlow, Databases, Reinforcement Learning, Open-source Software (OSS), Scientific Data Analysis, System Design, Artificial Intelligence (AI), Data Visualization, Matplotlib, SQL, REST APIs, Jupyter, Genetic Algorithms, Optimization Algorithms

Python Developer | Data Engineer

2022 - 2022
Budgie Health Inc.
  • Migrated a Jupyter notebook-based data cleaning logic to an optimized Python-based logic, reducing the run time by more than 10x.
  • Implemented a parser so that rules for data cleaning can be written in a simple text file and be modified without changing the code.
  • Worked with founders to identify the glitches in the codebase during the launch of the first product.
Technologies: Python, Jupyter Notebook, Data Engineering, ETL, Jupyter

Data Scientist

2022 - 2022
Ribbon Home, Inc.
  • Developed mechanisms for improving the filtering of the listings that the algorithm can automatically price.
  • Investigated the usage of NLP algorithms for extracting features from the listing information, which can be used to flag if any listing should be avoided.
  • Advised the company on the usage of ranking algorithms for developing a labeled dataset of comparable listings.
Technologies: Data Science, Data Analysis, Statistical Analysis, Model Development, Deep Learning, SQL, Python, TensorFlow, Pandas, PyTorch, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Classification Algorithms, Data Visualization, Matplotlib

AI Scientist

2016 - 2017
  • Developed a library to provide explainability for deep learning models. More specifically, we created deep learning models for diagnosing chest x-rays and implemented state-of-the-art methods for explaining the model's inference.
  • Created deep learning models for detecting early biomarkers in brain MRIs for Alzheimer's and segmentation models for ultrasound images to detect certain nerves in the neck.
  • Built a prototype of the first product for testing deep learning models for chest x-ray diagnosis and deployed it in a hospital in India. Worked with doctors in the hospital for regular feedback to make it user-friendly.
  • Wrote technical blogs for the company, presented the research at several conferences, and organized one of the largest artificial intelligence (AI) meetups in Mumbai.
Technologies: Computer Vision, Deep Learning, Medical Imaging, Python, PyTorch, Data Science, Jupyter Notebook, Image Processing, Databases, Open-source Software (OSS), Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Explainable Artificial Intelligence (XAI), Artificial Intelligence (AI), Data Visualization, Matplotlib, SQL, Healthcare Services, Keras, REST APIs, Jupyter

Explainable Deep Learning with Few Lines of Code
Developed a Python library with several state-of-the-art explainability methods that can plugin into any deep learning image classifier and would show the parts of the image that influence the decision. This provided an insight into what the models were learning.

The library was executed to be easily installed and usable with any PyTorch deep learning classifier. It included more than four deep learning methods for explainability developed based on the published research.

OPAL Project
Developed a large-scale privacy-preserving analytics platform with a small team located worldwide. The platform allows authorized users to use telecom data to generate meaningful insights like population density in several regions. Policymakers then used these insights in those countries.

The platform was deployed in Senegal and Colombia with our telecom partners.

Robustness of Perceptual Hashing Algorithms
We devised a new methodology to evaluate the robustness of perceptual hashing algorithms used to detect similar images. We showed that an attacker could modify the image such that it visually looks similar to the original image but cannot be detected by the matching perceptual hashing algorithm. Our methodology and results have been published at a top-ranked security conference.

Comparing Evolutionary Strategies for Othello
A project to evaluate several evolutionary strategies, such as particle-swarm optimization and covariance matrix adaptation-based strategies. We built an environment to simulate 2-player Othello agents, similar to an Open-AI gym. This simulator was then used to train and evaluate several different agents.

UNL for Language Translation

Design an NLP algorithm to extract rules from a language to convert it to UNL (Universal Natural Language) format. UNL is often seen as the HTML of future linguistics. One way to do machine translation would be to encode a language to a UNL format and decode UNL to any other language. Based on this idea, we built a rule-based converter for Hindi to UNL. We then used search to extract cases where rules failed and then used them to increase the rules in the dataset, thus creating a more comprehensive set of rules that allowed us to create machine translation models when there are not enough datasets for deep learning models.

We used tools like NLTK, Stanford Parser, Tokenizers, and others to achieve our goals.
2012 - 2016

Bachelor's Degree in Computer Science

Indian Institute of Technology Bombay - Mumbai, India


PyTorch, NumPy, Matplotlib, Scikit-learn, Node.js, TensorFlow, Keras, REST APIs, Stanford NLP, Pandas


Slack, Jupyter, Scikit-image


Python, SQL, R


Data Science, ETL


Jupyter Notebook, Visual Studio Code (VS Code)






Deep Learning, Computer Science, Machine Learning, Computer Vision, Explainable Artificial Intelligence (XAI), System Design, Reinforcement Learning, Open-source Software (OSS), Artificial Intelligence (AI), Data Visualization, Deep Reinforcement Learning, Medical Imaging, Data Privacy, Analytics, Natural Language Processing (NLP), Data Analysis, Genetic Algorithms, Optimization Algorithms, Combinatorial Optimization, FAISS, Image Processing, Perceptual Hashing, Multiprocessing, Evolutionary Algorithms, Scientific Data Analysis, Statistical Analysis, Model Development, Classification Algorithms, Data Engineering, Healthcare Services, Generative Pre-trained Transformers (GPT)

Collaboration That Works

How to Work with Toptal

Toptal matches you directly with global industry experts from our network in hours—not weeks or months.


Share your needs

Discuss your requirements and refine your scope in a call with a Toptal domain expert.

Choose your talent

Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.

Start your risk-free talent trial

Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.

Top talent is in high demand.

Start hiring