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Sr Software/ML/DL Engineer | LLMs | Agentic AI/RAG | High-Performance & Low-Latency Systems | Python, C++, SQL
An AI Scientist with over 9 years of experience in machine learning, deep learning, LLMs, biotechnology, and software development with a strong mathematical background. Current interests: - AI/ML research, deep learning, NLP and LLMs, data visualization, biotechnology. Technical Skills: - Languages/Packages: Python, modern JavaScript, MATLAB, modern C++, SQL (PL/SQL) - Libraries: TensorFlow, PyTorch, Plotly, CUDA, STL, Multithreading - Domains: Deep Learning, NLP, Classic ML Algorithms, Transformers (BERT, LLMs), CNN, RNN, LSTM, Autoencoders, Generative Models, Diffusion Models, Camera ISP - Operating Systems: Linux, Windows, macOS - Tools: VS Code, Jenkins, Docker, Git, Repo, Agile, Jira - Cloud Services: AWS EC2, AWS S3, Google Cloud, Azure
Ferdowsi University of Mashhad
Ph.D, Artificial Intelligence
October 1, 2021 – Present
Toussi The Globe Inc
AI/ML/C++ Engineer
June 1, 2023 – January 1, 2026
Toronto, Ontario, Canada · Hybrid
Ford Motor Company
Sr C++ Engineer
February 1, 2023 – May 1, 2023
Ontario, Canada · Remote
Planitar Inc.
Advanced Image Processing Researcher
June 1, 2022 – September 1, 2022
Regional Municipality of Waterloo, Ontario, Canada · On-site
Islamic Azad University of Mashhad
Sr Software Engineer, Team Lead
February 1, 2007 – September 1, 2019
On-site
Sentiment Analysis with Azure and GPT APIs
March 1, 2025 – March 1, 2025
This web application allows users to submit feedback, analyze its sentiment using Azure Cognitive Services, and receive AI-generated responses using GPT-3.5. Built with Python and Flask, it features a simple interface for feedback submission and sentiment analysis. The application also integrates text-to-speech services to provide audio responses that reflect the detected sentiment and emotions. Users can view sentiment results, access the LLM-generated response, and play or download the corresponding audio. Designed with a focus on functionality and clean code, the project ensures an intuitive and efficient user experience.
Making a new set of high-quality word vectors
April 1, 2021 – October 1, 2021
The goal of this project was to create a new set of high-quality word vectors using eigenwords and deep learning methods.
Accurate and fast matrix decomposition for low-rank learning
June 1, 2019 – September 1, 2020
Traditional SVD is an accurate algorithm for matrix decomposition that has many applications in different sciences. This algorithm is not a suitable choice for problems that involve huge input matrices because of its computational complexity. Randomized algorithms are a group of methods that tackle this problem by decomposing a smaller matrix derived from the main matrix. They successfully reduced the computational complexity of the traditional SVD algorithm and hence can be used with huge input matrices. However, such randomized algorithms may be inaccurate, or may require known values for key parameters. Using key concepts from Krylov subspaces, we have devised SVD algorithms that executes quickly and provide accurate, reliable singular values and corresponding singular vectors for huge input matrices.
Python Programming Assessment (Score: 90/100)
Feenyx
June 24, 2026 – Present
Generative AI: Working with Large Language Models
June 24, 2026 – Present
C++ PROGRAMMING (C++17 ISO STANDARD)- Score: 85 out of 100
IKM TeckChek
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, including deep learning, NLP, and matrix decomposition, along with experience in both academic and industry settings (Ford, Planitar, Toussi The Globe), suggests adaptability and a broad interest in AI/ML applications. The Ph.D. and research-oriented projects indicate a strong drive for innovation and problem-solving, which could be a good cultural fit for a research-heavy or innovative team. The long tenure in a previous role also suggests stability.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a focus on functionality, clean code, and intuitive user experience, suggesting attention to detail and user-centric design. The team lead experience implies leadership and collaboration skills. However, without specific psychometric test results or interview data, a detailed assessment of soft skills and operational fit is limited.