This is my newer webpage for 2025 and onwards! My previous blogs and webpage is still available here if you prefer till I complete the migration.
I am a seasoned Machine Learning Engineer at Bell Canada, currently working on a number of applied ML research problems. Previously, I worked at Amazon as a Machine Learning Data Associate for Alexa AI, a conversational technology that backs various core NLU services offered by Amazon.
I graduated from the University of Toronto with a Masters in Computer Engineering specializing in Machine Learning , Data Science and Cloud Computing. I am also a Google certified professional ML Engineer, AWS Certified Solutions Architect, and AWS Certified Machine Learning Specialist and I absolutely love teaching Machine learning and Data Science!
I’ve taught a number of AI and software engineering courses at the University of the Toronto, you can read more about these on my teaching page.
Experience
Machine Learning Engineer Jan 2022 - Present
Bell Toronto
- Developed and deployed a unified AI platform, enabling rapid prototyping and deployment of AI solutions across multiple business units. This involved fine‑tuning pre‑trained models, building foundational APIs, and streamlining the AI development workflow.
- Architected and optimized distributed compute environments for running large‑scale language models, implementing data and model parallelism techniques to efficiently handle massive datasets and reduce training time
- Built a Semantic Search Engine using Transformers to retreive relevant multimodal data from various data stores for downstream ML tasks
- Engineered and implemented advanced deep learning models for anomaly detection in Kubeflow for network and probes data in Kafka pipelines, achieving a 78% reduction in the time required to detect critical first‑party data incidents.
- Developed a Generative AI Diagnostics Assistant using retrieval augmented generation method backed by finetuned Llama2 model. Resulted in network technicians efficiently conducting diagnostics, reducing resolution time from 17 minutes to 4 minutes. (∼ 76.47% reduction)
- Implemented robust LLMOps practices, leveraging MLFlow for experiment tracking and Databricks ML for managing machine learning workflows, resulting in a 25% reduction in model deployment time and improved reproducibility of results
Related Resources:
Teaching Assistant Sep 2020 - Present
University of Toronto Toronto
- APS360 Applied Fundamentals of Machine Learning, University of Toronto
- ECE1762 Special Topics in Software: Empirical Methods, University of Toronto
- ECE444 Software Engineering, University of Toronto
Data Engineer May 2020 - May 2021
University Health Network Toronto
- Worked with Dr. Istvan Mucsi’s KHE Research group. Designed, built and maintained research participant’s databases. Created ELT pipelines for Data Analysis replacing the conventional ETL pipelines which improved the ELT process efficiency by over 5%.
Machine Learning Data Associate May 2018 - July 2019
Amazon
- Collaborated with Alexa AI Research Scientists to develop and deploy cutting‑edge NLP models , directly contributing to the performance of core Natural Language Understanding (NLU) services powering Alexa. This involved extensive model training, testing, and evaluation.
- Designed and implemented frameworks and pipelines that streamlined model prototyping, leading to an impressive 85% reduction in time required for experimentation and iteration
- Prioritized strict compliance with regulatory requirements and contributed to software tool improvements by resolving bugs and suggesting enhancements, resulting in over a 5% improvement in first‑iteration model quality metrics.
Industry
Google Certified Professional ML Engineer March 2024 - Present
- A Professional Machine Learning Engineer builds, evaluates, productionizes, and optimizes ML models by using Google Cloud technologies and knowledge of proven models and techniques. The ML Engineer has strong programming skills and experience with data platforms and distributed data processing tools. The ML Engineer is proficient in the areas of model architecture, data and ML pipeline creation, and metrics interpretation. The ML Engineer is familiar with foundational concepts of MLOps, application development, infrastructure management, data engineering, and data governance.
AWS Machine Learning Speciality June 2021 - Present
Amazon Web Services
- This certificate exam tests a developer's foundational knowledge of integrating machine learning into tools and applications. The certificate program requires an understanding of building TensorFlow models using Computer Vision, Convolutional Neural Networks, Natural Language Processing, and real-world image data and strategies.
AWS Solutions Architect June 2021 - Present
Amazon Web Services
- Advanced. Earners of this certification have an in-depth understanding of AWS machine learning (ML) services. They demonstrated ability to build, train, tune, and deploy ML models using the AWS Cloud. Badge owners can derive insight from AWS ML services using either pretrained models or custom models built from open-source frameworks.