Reza Fazeli, Developer in Toronto, ON, Canada
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Reza Fazeli

Verified Expert  in Engineering

Machine Learning Engineer and Developer

Location
Toronto, ON, Canada
Toptal Member Since
October 19, 2023

Reza is a highly skilled machine learning engineer with 11 years of experience. Currently working at IBM, he has developed and deployed cutting-edge machine learning algorithms to enhance virtual assistant products. His research at the University of Toronto focused on numerical modeling techniques for solving multiphase flow problems. With a strong background as a machine learning engineer and data scientist, Reza's expertise lies in natural language processing and computer vision.

Portfolio

Law of the Jungle Pty Limited
Artificial Intelligence (AI), Leadership, Code Review, Source Code Review...
IBM
Python, Machine Learning, Language Models, Natural Language Processing (NLP)...
SoundHound
Python, Machine Learning, Language Models, PyTorch, Kubernetes, Docker...

Experience

Availability

Full-time

Preferred Environment

MacOS, Slack, Visual Studio Code (VS Code), Linux

The most amazing...

...thing I've deployed is a transformer-based model for paraphrasing irregular user utterances, significantly improving the quality of responses to user questions.

Work Experience

AI Tech Advisor

2023 - PRESENT
Law of the Jungle Pty Limited
  • Designed experimentation and led the development of a machine learning model for text classification, enhancing claim detection in ad campaigns across various industries and improving accuracy by 11% over the baseline model.
  • Implemented an initial vector-based search system to detect and group similar claims across different campaigns, enhancing user experience and reducing manual work by up to 72%.
  • Assessed risks and gaps, proposed quick-win machine learning (ML) solutions, and evaluated deviations from best practices to optimize processes.
Technologies: Artificial Intelligence (AI), Leadership, Code Review, Source Code Review, Document Parsing, ChatGPT, Prompt Engineering

Machine Learning Engineer

2021 - PRESENT
IBM
  • Developed and deployed machine learning solutions using large language models to enhance text classification and automate model improvement based on user feedback with limited data, resulting in six patents.
  • Created and implemented clustering algorithms to unify unrecognized utterances into intents for model training.
  • Built a unified metrics framework to evaluate improvements in machine learning algorithms for text classification, resulting in a published journal article.
Technologies: Python, Machine Learning, Language Models, Natural Language Processing (NLP), Docker, Kubernetes, PyTorch, TensorFlow, Elasticsearch, Visual Studio Code (VS Code), Linux, Git, GitHub, SQL, Artificial Intelligence (AI), APIs, Large Language Models (LLMs), API Integration, Generative Pre-trained Transformer 3 (GPT-3), gRPC, Leadership, Data Science, AI Programming, Deep Learning, Algorithms, Analytics, Big Data, Unstructured Data Analysis, CSV File Processing, Generative Pre-trained Transformers (GPT), Data Analytics, Data Analysis, Mathematics, Chatbots, Chatbot Conversation Design, OpenAI, Technical Leadership, Mentorship & Coaching, Deep Neural Networks, AI Modeling, Object-oriented Programming (OOP), Research, BERT, Predictive Modeling, AI Model Training, Optimization, Generative Artificial Intelligence (GenAI), Hugging Face, Open-source LLMs, Minimum Viable Product (MVP), OpenAI GPT-3 API, OpenAI GPT-4 API, Generative AI, Streamlit, SpaCy, Supervised Learning, Synthetic Data Generation, Scikit-learn, Code Review, Source Code Review, LangChain, ChatGPT, Vectorization, Document Parsing, AI Agents, Retrieval-augmented Generation (RAG), Machine Learning Operations (MLOps), Multi-agent Systems, Reinforcement Learning from Human Feedback (RLHF), Prompt Engineering

Machine Learning Engineer

2020 - 2021
SoundHound
  • Led a team of four in developing sequence-to-sequence models using PyTorch to rewrite irregular questions to well-structured ones, helping our natural language understanding (NLU) layer understand poorly structured questions.
  • Guided a team of three in creating phrase suggestion models given a list of keywords using pre-trained transformers-based language models.
  • Developed a neural retrieval system coupled with a sequence-to-sequence model to respond to factual queries.
Technologies: Python, Machine Learning, Language Models, PyTorch, Kubernetes, Docker, Natural Language Processing (NLP), TensorFlow, Visual Studio Code (VS Code), Linux, Git, GitHub, SQL, Artificial Intelligence (AI), APIs, Large Language Models (LLMs), API Integration, Generative Pre-trained Transformer 3 (GPT-3), Leadership, Data Science, Deep Learning, Algorithms, Analytics, Big Data, Unstructured Data Analysis, CSV File Processing, Generative Pre-trained Transformers (GPT), Data Analytics, Data Analysis, Mathematics, Chatbots, Technical Leadership, Mentorship & Coaching, Deep Neural Networks, AI Modeling, Object-oriented Programming (OOP), Research, BERT, Predictive Modeling, AI Model Training, Optimization, Generative Artificial Intelligence (GenAI), Hugging Face, Open-source LLMs, Minimum Viable Product (MVP), Generative AI, Streamlit, SpaCy, Supervised Learning, Synthetic Data Generation, Scikit-learn, Fine-tuning, Code Review, Source Code Review, Vectorization, Machine Learning Operations (MLOps)

Data Scientist

2019 - 2020
Looka
  • Led a team of three data scientists and data engineers to build Looka's recommendation and smart suggestion engines using Elasticsearch, increasing the offline conversion rate, specifically user sign-up to package purchase, from 5% to 9%.
  • Trained deep generative models like convolutional VAEs and GANs in PyTorch and RNNs in TensorFlow to generate design assets, allowing users to access high-quality, royalty-free fonts and symbols.
  • Supervised two data science interns over four months, helping generate over 1,000 design assets such as fonts, symbols, and layouts using deep generative models developed in PyTorch and TensorFlow and trained on AWS.
Technologies: Python, Elasticsearch, Generative Adversarial Networks (GANs), Variational Autoencoders, Recurrent Neural Networks (RNNs), AWS CLI, TensorFlow, Computer Vision, PyTorch, Visual Studio Code (VS Code), Linux, Git, GitHub, SQL, Artificial Intelligence (AI), MySQL, Data Science, Deep Learning, Algorithms, Analytics, Unstructured Data Analysis, CSV File Processing, Data Analytics, Data Analysis, Mathematics, Deep Neural Networks, Image Generation, AI Modeling, Object-oriented Programming (OOP), Research, Predictive Modeling, AI Model Training, Optimization, Generative Artificial Intelligence (GenAI), Minimum Viable Product (MVP), Image Processing, Streamlit, Text Recognition, Supervised Learning, Synthetic Data Generation, Scikit-learn, Amazon Web Services (AWS), Multimodal GenAI, Code Review, Source Code Review, Vectorization

Data Scientist

2017 - 2018
B-Secur
  • Collaborated with a team of four data scientists to create an in-house development framework for building pattern recognition algorithms aimed at a wide range of biometric applications based on the electrocardiogram (ECG).
  • Developed pattern recognition algorithms using techniques such as principal component analysis, linear discriminant analysis, and autoencoders for feature learning, k-nearest neighbors, support vector machines, and neural networks for classification.
  • Trained a convolutional variational autoencoder using TensorFlow and optimized it for maximum performance and minimal memory footprint for deployment on ARM Cortex-M CPUs targeted for intelligent IoT edge devices.
Technologies: Python, MATLAB, Machine Learning, Signal Processing, Visual Studio Code (VS Code), Linux, Git, GitHub, Artificial Intelligence (AI), Data Science, Deep Learning, Algorithms, Analytics, Unstructured Data Analysis, CSV File Processing, Data Analytics, Data Analysis, Mathematics, Deep Neural Networks, AI Modeling, Object-oriented Programming (OOP), Research, Predictive Modeling, AI Model Training, Optimization, Minimum Viable Product (MVP), Supervised Learning, Scikit-learn, Code Review, Source Code Review

Research Scientist

2016 - 2017
Unity Health Toronto
  • Developed SVM and MLP models for classifying neonate breathing patterns based on features extracted from the electrical activity of the diaphragm, offering an unbiased approach for categorizing neonate breathing.
  • Implemented signal processing algorithms in Python and C for real-time signal analysis on the Arduino platform.
  • Communicated analytical insights to industry partners using interactive dashboards developed in Excel VBA, securing more than $100,000 in research funding.
Technologies: Python, MATLAB, Excel VBA, Arduino, Digital Signal Processing, Visual Studio Code (VS Code), Linux, Git, GitHub, Artificial Intelligence (AI), Data Science, Algorithms, Analytics, CSV File Processing, Data Analytics, Data Analysis, Mathematics, Object-oriented Programming (OOP), Research, Optimization, Code Review, Source Code Review

Software Engineer

2013 - 2015
University of Toronto
  • Developed an open-source numerical modeling software (OpenPNM) in an international collaboration between four academic institutions, providing over 100 scientists worldwide with a framework for performing multiphase simulations.
  • Developed, tested, and executed parallelized C++ codes on SciNet, a high-performance computing network running on Unix, allowing for the analysis of large datasets within the 48-hour job execution time limit of SciNet clusters.
  • Performed unit tests using the Python unit testing framework, unittest, and prepared code documentation using Sphinx.
Technologies: Python, Git, GitHub, Open Source, C++, Parallel Programming, OpenMP, Numerical Modeling, Visual Studio Code (VS Code), Linux, MATLAB, C, Algorithms, Analytics, CSV File Processing, Data Analytics, Data Analysis, Mathematics, Object-oriented Programming (OOP), Optimization, Image Processing, Code Review, Source Code Review

Automated Dataset Pairing Algorithm

Developed cluster-based algorithms to pair poorly structured queries with semantically similar, well-structured queries. This approach enabled the creation of high-quality training data for building paraphrasing models.

Automated Virtual Assistant Learning

Developed and deployed machine learning solutions using transformer-based language models to enhance text classification and automate model improvement based on user feedback with limited data, resulting in six patents.

Automated Font and Symbol Generation

Trained various deep generative models such as convolutional variational autoencoders (CVAEs) and generative adversarial networks (GANs) in PyTorch and RNN models in TensorFlow for design asset generation, providing users access to high-quality, royalty-free fonts and symbols.
2013 - 2015

Master's Degree in Engineering

University of Toronto - Toronto, Ontario, Canada

Libraries/APIs

PyTorch, TensorFlow, SpaCy, Scikit-learn, OpenMP

Tools

Slack, Git, GitHub, ChatGPT, AWS CLI, MATLAB

Frameworks

Streamlit, gRPC

Languages

Python, SQL, C, Excel VBA, C++

Paradigms

Data Science, Object-oriented Programming (OOP), Synthetic Data Generation, Parallel Programming

Platforms

MacOS, Visual Studio Code (VS Code), Docker, Kubernetes, Linux, Arduino, Amazon Web Services (AWS)

Storage

Elasticsearch, MySQL

Other

Machine Learning, Language Models, Natural Language Processing (NLP), Computer Vision, Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNN), Artificial Intelligence (AI), APIs, Large Language Models (LLMs), Generative Pre-trained Transformer 3 (GPT-3), AI Programming, Deep Learning, Algorithms, Analytics, Big Data, Unstructured Data Analysis, CSV File Processing, Generative Pre-trained Transformers (GPT), Data Analytics, Data Analysis, Mathematics, Chatbots, Chatbot Conversation Design, OpenAI, Technical Leadership, Mentorship & Coaching, Deep Neural Networks, Image Generation, AI Modeling, Research, BERT, Predictive Modeling, AI Model Training, Optimization, Generative Artificial Intelligence (GenAI), Hugging Face, Open-source LLMs, Minimum Viable Product (MVP), OpenAI GPT-3 API, OpenAI GPT-4 API, Generative AI, Image Processing, Text Recognition, Supervised Learning, Code Review, Source Code Review, Vectorization, Document Parsing, AI Agents, Retrieval-augmented Generation (RAG), Machine Learning Operations (MLOps), Multi-agent Systems, Reinforcement Learning from Human Feedback (RLHF), Prompt Engineering, Generative Adversarial Networks (GANs), Variational Autoencoders, Signal Processing, Digital Signal Processing, Open Source, Numerical Modeling, API Integration, Leadership, Fine-tuning, LangChain, Multimodal GenAI, Text-to-text Transfer Transformer (T5)

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