Curriculum Vitae

Senior MLOps Engineer

Precision AI

Building and shipping multimodal AI, from computer vision and vision-language models to LLMs and generative systems, end to end.

• Build and optimize multimodal AI systems spanning computer vision, vision-language models, and large-scale generative models, including CNNs, ViTs, diffusion models, LLMs, and segmentation architectures using PyTorch Lightning, HuggingFace, vLLM, and Nomad orchestration.
• Apply advanced modeling techniques including transfer learning, parameter-efficient fine-tuning, supervised fine-tuning, prompt engineering, and multimodal fusion to improve model performance across production workloads.
• Develop and evaluate agentic AI systems leveraging RAG pipelines, structured reasoning workflows, and multi-step tool-using architectures informed by recent research and experimental prototypes.
• Design end-to-end MLOps pipelines covering dataset curation, large-scale annotation workflows, synthetic data generation, training orchestration, evaluation frameworks, and production deployment with MLflow-based tracking and model governance.
• Implement efficient inference and optimization strategies including quantization, pruning, distillation, and model compression to support scalable real-time and edge deployment scenarios.
• Build and maintain cloud-native AI systems on AWS using Docker, Kubernetes, CI/CD pipelines, and CodeArtifact, enabling reproducible training environments and scalable inference infrastructure.
• Design and integrate production APIs (REST/GraphQL) to serve ML models within larger distributed systems and data-driven applications.
• Collaborate on system reliability through structured experimentation, evaluation pipelines, and iterative model improvement informed by ongoing research literature.

Machine Learning Engineer

TerraSense Analytics Ltd.

Owned ML models and pipelines end to end, ran the product scrum team, and pioneered synthetic data and synthetic aperture radar research.

• Designed, collected, and managed machine learning datasets and the data pipeline.
• Developed, implemented, documented, and maintained software programs, machine learning models, and their training, validation, and deployment pipelines.
• Created CI/CD processes for core product images and release management of the core product.
• Continuously monitored success metrics of assigned ML projects, making continuous improvements to model robustness and efficiency.
• Deployed ML algorithms in conjunction with the software team and implemented tools to monitor performance.
• Developed the core components of the synthetic data rendering pipeline using NVIDIA Omniverse.
• Pioneered research for synthetic aperture radar capabilities from raw data to mission management systems.
• Developed automated testing platforms for product system testing.
• Served as scrum master of a small product-focused team: assisted the product owner in planning quarterly goals and bi-weekly sprints, and hosted daily standups.
• Coordinated work for and managed the part-time data labelling team.
• Attended data collection events with TerraSense partners and operated the Wescam MX-15 sensor.

Sessional Instructor

Ontario Tech University

Lead instructor of record for three computer science courses, reaching 350+ students across data structures, software design, and compilers.

• Prepared and deployed course materials for lectures, laboratories, examinations, and assignments.
• Conducted weekly lecture sessions (3 hours per week) in line with the developed syllabus, hosted 1-on-1 office hours, and stayed available for student questions.
• Courses taught:
  • CSCI 2010U - Data Structures, Fall 2021 (146 students over 2 sections)
  • CSCI 2040U - Software Design and Analysis, Winter 2022 (130 students)
  • CSCI 4020U - Compilers, Winter 2022 (74 students)

Teaching Assistant

Ontario Tech University

• Led laboratory and tutorial sessions for undergraduate courses; marked assignments and exams with attention to detail.
• Courses: Introduction to Programming for Computer Scientists, Introduction to Programming for Scientists (2), Scientific Data Analysis, Software Design and Analysis, Computer Architecture, Analysis of Algorithms (2), Compilers, Interactive Media, and Computer Vision (2).

Student Researcher

Ontario Tech University

• VCLab, Visual Computing Lab (Apr 2018 - Dec 2021), supervised by Dr. Faisal Qureshi:
  • Developed a Python framework using PyTorch for content-aware video summarization using high- and low-level image processing with various neural network models.
  • Conducted research on biological diagram understanding to create the next generation of bioinformatics tools using PyTorch.
  • Collaborated with bioinformatics graduate students at the University of Toronto to ensure the product met their expectations.
• Vialab, Visualization for Information Analysis Lab (May 2017 - Apr 2018), supervised by Dr. Christopher Collins:
  • Developed a front-end JavaScript / D3 dashboard for the Registrar's Office to help find problem areas within every faculty and program, focusing on student retention.

Computer Science Instructor

Explorer Robotics

• Developed five full courses teaching elementary-level students the fundamentals of computer science, delivered in person and online: Programming Fundamentals, Advanced Programming, Game Development in Pygame, Problem Solving using Programming, and Object-Oriented Programming.
• Lessons were conducted in Python and supplemented with whiteboard activities to explain high-level concepts.

Swing Manager

McDonald's Ajax

Climbed from crew member to swing manager in four years, leading teams of 15 to 20 in a high-tempo kitchen.

• Led crews of 15 to 20 in a fast-paced environment, ensuring restaurant safety and production standards while shadowing crew across service, production, and guest experience.
• Ensured correct food cooking procedures and safety criteria were met, tracking temperatures, times, and batch amounts between each meal changeover.
• Tracked inventory through clear view: one week of product on hand, everything within expiration date, and proper food handling protocols followed.

Line Cook

Chuck's Roadhouse

• Worked in a small brigade de cuisine preparing meals for guests, cooking proteins to requested temperatures under food safety guidelines.

Master of Science, Computer Science

Ontario Tech University

• Thesis: Parsing Genetic Models, defended December 8, 2021.
• GPA: 4.0
• Notable courses: Graduate Computer Vision, Global A.I. Ethics, Advanced Topics in Information Science.

Bachelor of Science (Honours), Computer Science

Ontario Tech University, Data Science specialization

• Thesis: Exploring LSTMs for Video Analysis, defended April 18, 2019.
• GPA: 3.6, graduated with Distinction.
• Notable courses: Computer Vision, Big Data Analytics, Information Visualization, Analysis of Algorithms, Simulation and Modelling, Computational Science.
• Dean's List in Winter 2017 and Fall 2017; President's List in Winter 2018 and Winter 2019.

Ontario Secondary School Diploma

Notre Dame Catholic Secondary School

• 2014-2015 Ontario Scholar; Certificate of Academic Achievement, Computer Studies Award; Honour Roll for four consecutive years.

Parsing Genetic Models

M.Sc thesis, VCLab, supervised by Dr. Faisal Qureshi

• Funded by Genome Canada with collaborators at the University of Toronto, building the next generation of bioinformatics tools. Computer vision and deep learning are applied to biological diagrams using PyTorch and OpenCV in a full pipeline that processes a given biological diagram into a complete list of the genes and relationships present.

Exploring LSTMs on Video Analysis

B.Sc Honours thesis, VCLab, supervised by Dr. Faisal Qureshi

• A Python framework using PyTorch for content-aware video summarization with high- and low-level image processing across various neural network models, exploring which visual features are most effective to pass through LSTMs when deciding which frames belong in a video's summary.

RetentionVis

Undergraduate research, Vialab, supervised by Dr. Christopher Collins

• An exploratory data analysis tool built in JavaScript and D3 for the Registrar's Office at Ontario Tech University, visualizing large amounts of student retention data with an emphasis on patterns predictive of withdrawal. Interactive filters help the office answer the question: why are students dropping out?