Reza is available for hire
Hire RezaReza Fazeli
Verified Expert in Engineering
Machine Learning Engineer and Developer
Toronto, ON, Canada
Toptal member since October 19, 2023
Bio
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)...
Baran Yildiran
Machine Learning, Natural Language Processing (NLP), LLM, Code Review...
Experience
Availability
Part-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, Neural Networks, Transformers
Machine Learning Engineer
2021 - PRESENT
IBM
- Developed and deployed retrieval-augmented generation (RAG) solutions using Elasticsearch as the external knowledge base to ground large language models (LLMs) on the most precise and current data.
- Worked with a team of four developers and three researchers to develop ML solutions using large language models (LLMs) to enhance text classification and automate model improvement based on user feedback, resulting in six patents.
- Developed and deployed 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, LLM, 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 (DNNs), 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, 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, Vector Databases, Scalable Vector Databases, CI/CD Pipelines, Neural Networks, Transformers, OpenAI API
Machine Learning LLM Expert
2024 - 2024
Baran Yildiran
- Led the development of a technology demonstration tool, leveraging LLMs in an agentic flow to identify vulnerabilities in collaboration with traditional security tools. This project aimed at securing additional funding for scaling.
- Formulated and presented a technical vision for integrating security, privacy, and scalable frameworks for private and government use cases, simplifying complex content for executive presentations to drive strategic initiatives.
- Conducted literature review on LLM-based vulnerability detection methods, developed architectural plans, and designed experiments to evaluate LLM performance.
Technologies: Machine Learning, Natural Language Processing (NLP), LLM, Code Review, Source Code Review, Cybersecurity
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, LLM, 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 (DNNs), 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), Streamlit, SpaCy, Supervised Learning, Synthetic Data Generation, Scikit-learn, Fine-tuning, Code Review, Source Code Review, Vectorization, Machine Learning Operations (MLOps), CI/CD Pipelines, Neural Networks, Transformers
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 (DNNs), 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, CI/CD Pipelines, Neural Networks
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 (DNNs), 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, Neural Networks
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
Experience
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.
Education
2013 - 2015
Master's Degree in Engineering
University of Toronto - Toronto, Ontario, Canada
Skills
Libraries/APIs
PyTorch, TensorFlow, SpaCy, Scikit-learn, OpenAI API, OpenMP
Tools
Slack, Git, GitHub, ChatGPT, AWS CLI, MATLAB
Languages
Python, SQL, C, Excel VBA, C++
Frameworks
Streamlit, gRPC
Paradigms
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
Industry Expertise
Cybersecurity
Other
Machine Learning, Language Models, Natural Language Processing (NLP), Computer Vision, Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Artificial Intelligence (AI), APIs, LLM, Generative Pre-trained Transformer 3 (GPT-3), 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 (DNNs), 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, 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, Vector Databases, Scalable Vector Databases, CI/CD Pipelines, Neural Networks, Transformers, 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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