Tanvir is available for hire
Hire TanvirTanvir Sajed
Verified Expert in Engineering
Back-end Developer
Location
Vancouver, Canada
Toptal Member Since
August 30, 2022
Tanvir is a highly skilled software developer and researcher with seven years of experience seeking either Ruby on Rails or machine learning projects. He is a very passionate and meticulous professional, always eager to learn new knowledge and technologies. Tanvir takes ownership, delivers on time, and aims for the best possible outcome in any given scenario.
Portfolio
Self-employed
Ruby on Rails (RoR), MySQL, Redis, NGINX, Capistrano, Puma, Administration...
Huawei Technologies Co.
Machine Learning, Deep Reinforcement Learning, Heuristics, PyTorch, TensorFlow...
Drugbank
Ruby on Rails (RoR), Python 3, JSON REST APIs, Sidekiq, Redis, Elasticsearch...
Experience
Availability
Part-time
Preferred Environment
Visual Studio Code (VS Code), Microsoft Teams, Linux, Ruby on Rails (RoR), Python 3, Docker
The most amazing...
...tool I've developed is a full-stack ML application presented at the distinguished NLP conference of the Association for Computational Linguistics.
Work Experience
Ruby on Rails Engineer
2021 - PRESENT
Self-employed
- Developed, maintained, and updated Phytohub, a plant information related web application using Ruby on Rails.
- Developed back-end data pipelines that import plant-related datasets from online resources.
- Built front-end interface using JavaScript, CoffeeScript, and Slim.
- Worked with Redis, NGINX, Puma, Capistrano, JSON, RSpec, MySQL, Active Admin, and Digital Ocean.
- Deployed the Phytohub application on distributed servers that are being used by researchers at INRAE and other agriculture-based research institutions.
Technologies: Ruby on Rails (RoR), MySQL, Redis, NGINX, Capistrano, Puma, Administration, DigitalOcean, JavaScript, CoffeeScript, JSON API, Code Review, Ruby, HTML, Git, REST APIs, CSS, API Integration, Bootstrap, Full-stack, API Development, Back-end, Test-driven Development (TDD)
Artificial Intelligence Engineer
2020 - 2022
Huawei Technologies Co.
- Researched and developed deep reinforcement learning technologies with MCTS, UCB, Bandits, DQN, AlphaZero, AlphaGo, and ResNets.
- Developed ML and RL end-to-end frameworks using Python, C++, TensorFlow, PyTorch, TVM, and Halide.
- Trained very large deep neural networks, including ResNets, using GPU parallelization.
- Deployed a deep RL-based product to GPU, CPU, and NPU servers being used in production.
- Published a paper in ICTAI improving the state-of-the-art results in tensor optimization (TVM) using a Bandit-based reinforcement learning algorithm.
- Published a heuristic search paper in SoCS based on memory-bounded best-first beam search for scheduling halide programs.
Technologies: Machine Learning, Deep Reinforcement Learning, Heuristics, PyTorch, TensorFlow, Monte Carlo Tree Search (MCTS), Residual Neural Network (ResNet), Deep Neural Networks, Beam Search, Genetic Algorithms, Python 3, C++11, Source Code Review, Code Review, Python, Artificial Intelligence (AI), Deep Learning, Git, API Integration, CI/CD Pipelines, Back-end, Test-driven Development (TDD)
Software Developer
2019 - 2020
Drugbank
- Developed, maintained, and updated a large-scale drug information SaaS Drugbank application with one million daily users using Ruby on Rails.
- Developed back-end data pipelines that import drug-related datasets from online resources.
- Built internal software libraries and RESTful APIs to facilitate biological data exchange using Her and Faraday libraries.
- Worked with Sidekiq, Redis, Elasticsearch, Puma, Unicorn, NGINX, Capistrano, Haml, JSON, RSpec, MySQL, Active Admin, AWS, Google Cloud Engine, New Relic, AppSignal.
- Developed front-end JavaScript and HTML using JQuery, Slim, ERB, and CoffeeScript.
- Implemented SVM, RNN, Transformer, skip-gram, and BOW models for a multi-classification task to predict the type of interactions between drugs and proteins from natural language.
- Implemented spaCy NER classification algorithm to predict different entities like drugs and proteins from the text. Implemented BERT from PyTorch-Transformers for this task.
Technologies: Ruby on Rails (RoR), Python 3, JSON REST APIs, Sidekiq, Redis, Elasticsearch, MySQL, Capistrano, Puma, jQuery, JavaScript, New Relic, Google Cloud Engine, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), GPT, PyTorch, Transformers, BERT, SpaCy, Recurrent Neural Networks (RNN), Support Vector Machines (SVM), Source Code Review, Code Review, Python, Ruby, HTML, Artificial Intelligence (AI), Deep Learning, Git, REST APIs, CSS, API Integration, Google Cloud Platform (GCP), Bootstrap, Scrapy, Full-stack, API Development, Amazon Web Services (AWS), CI/CD Pipelines, Back-end, Test-driven Development (TDD)
Artificial Intelligence and Robotics Developer
2018 - 2019
A&K Robotics
- Researched and developed AI software that predicts objects and their distance from the camera and other sensors like LIDAR and IR, implementing YOLO, R-CNN, and SSD.
- Worked with Python, C++, TensorFlow, PyTorch, Detectron, and Darknet.
- Implemented firmware applications with Arduino and Raspberry Pi and UNIX-based systems on ROS.
- Implemented best practices of Agile software design, Trello cards and epics, pull requests, and GitHub issues.
Technologies: Darknet, Detectron, TensorFlow, PyTorch, Python 3, C++, Arduino, Raspberry Pi, Deep Neural Networks, Computer Vision, Robotics, Source Code Review, Code Review, Python, Artificial Intelligence (AI), Deep Learning, Image Processing, Git, Test-driven Development (TDD)
Software Developer in R&D
2014 - 2016
The Metabolomics Innovation Center
- Developed, maintained, and updated multiple web application software, servers and databases using Agile software engineering with a team of software developers.
- Created large-scale web applications to handle more than 300,000 users per month on Drugbank and Human Metabolome Database.
- Published five research papers, academic journals, and over ten bioinformatics application servers and databases using Ruby on Rails, MongoDB, MySQL, AngularJS, JQuery, Twitter Bootstrap, and Python Django.
- Developed internal software libraries and RESTful APIs to facilitate biological data exchange.
- Worked on system administration with cloud servers hosted on Google Compute Engine, Digital Ocean, and AWS.
Technologies: Ruby on Rails (RoR), MySQL, AngularJS, JavaScript, MongoDB, Bootstrap, Django, Python, Google Compute Engine (GCE), DigitalOcean, Puma, Capistrano, Redis, Elasticsearch, jQuery, JSON API, New Relic, SaltStack, NGINX, Agile, Source Code Review, Code Review, Ruby, HTML, Git, REST APIs, CSS, API Integration, Google Cloud Platform (GCP), Full-stack, API Development, Amazon Web Services (AWS), CI/CD Pipelines, Back-end, Test-driven Development (TDD)
Experience
Stimulating Creativity with FunLines
https://aclanthology.org/2020.acl-demos.28/FunLines is a competitive game where players edit news headlines to make them funny and where they rate the most comic of headlines edited by others. FunLines makes the humor generation process fun, interactive, collaborative, rewarding, and educational, keeping players engaged and providing humor data at a meager cost compared to traditional crowdsourcing approaches.
Based on the generated dataset, we trained a deep neural network model that can predict the degree of fun given a sentence. We then deployed this model in production, which has been used as a guide for users to know how fun their sentences would be. We trained the model using BERT and PyTorch. The application was built using the Python Django framework. The application was demoed and published at the Association for Computational Linguistics (ACL) NLP conference.
Based on the generated dataset, we trained a deep neural network model that can predict the degree of fun given a sentence. We then deployed this model in production, which has been used as a guide for users to know how fun their sentences would be. We trained the model using BERT and PyTorch. The application was built using the Python Django framework. The application was demoed and published at the Association for Computational Linguistics (ACL) NLP conference.
Skills
Languages
Python 3, Python, SQL, Ruby, HTML, CSS, JavaScript, CoffeeScript, C++11, C++
Frameworks
Ruby on Rails (RoR), Bootstrap, Django, Scrapy, Flask, Darknet, AngularJS
Libraries/APIs
PyTorch, REST APIs, API Development, jQuery, TensorFlow, JSON API, Sidekiq, SpaCy
Tools
Git, NGINX, Capistrano, Google Compute Engine (GCE), SaltStack
Paradigms
Test-driven Development (TDD), Agile
Platforms
Linux, Docker, DigitalOcean, Google Cloud Platform (GCP), Amazon Web Services (AWS), New Relic, Google Cloud Engine, Arduino, Raspberry Pi
Storage
MySQL, Redis, Elasticsearch, MongoDB, SQLite
Other
Machine Learning, Full-stack Development, Source Code Review, Code Review, Artificial Intelligence (AI), API Integration, Full-stack, CI/CD Pipelines, Back-end, Deep Reinforcement Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, Expert Systems, Puma, Administration, Heuristics, Monte Carlo Tree Search (MCTS), Residual Neural Network (ResNet), Beam Search, Genetic Algorithms, JSON REST APIs, Transformers, BERT, Recurrent Neural Networks (RNN), Support Vector Machines (SVM), Detectron, Deep Neural Networks, Robotics, Image Processing, GPT, Generative Pre-trained Transformers (GPT)
Education
2016 - 2021
Master's Degree in Computer Science
University of Alberta - Edmonton, AB, Canada
2010 - 2014
Bachelor's Degree in Computer Science
University of Alberta - Edmonton, AB, Canada