Muhammad Sohaib Saqib

Aspiring Computer Scientist who is always actively curious about new developments and innovations within the Computer Science and Technology sectors, especially within Artificial Intelligence. I am always open to new opportunities in Machine Learning and Software Development!

Currently a fourth year student at Trinity College, University of Toronto, and am pursuing a Specialist in Computer Science and Minor in Economics. Skilled in Python, Java, project initiation and leadership, event management and community service, along with some background into Machine Learning research.

Thoroughly enjoy fitness and hitting the gym regularly, along with Rowing and Badminton. Also immensely passionate about developments in World Affairs, shifting dynamics of Geopolitics, and current affairs of Pakistan.

As I continue to hone my technical and communication skills, I desire to keep on creating a constructive, positive impact on peoples' lives.

AMAZON:

As an intern at Amazon, I was tasked with an end-to-end project to build a machine learning automation pipeline using Java and Typescript that would facilitate data scientists and machine learning scientists at Amazon in the development of machine learning models. Such a project could be classified as an MLOps job.

Initially, I had to independently formulate a detailed design document that defined the project scope and architecture, including a thorough assessment of proposed components and features that would be part of the pipeline.

To develop the pipeline, I used native AWS components such as Lambda server-less compute, S3 cloud storage, and Step Functions workflow automation to build the pipeline. Furthermore, AWS Sagemaker, a toolkit for working on machine learning models, was also utilized for the pipeline.

As part of key software engineering practices, I incorporated design patterns such as Dependency Injection, Factory and Builders for software optimization, scalability, and extensibility. The design part took approximately three weeks to complete whereas the implementation part took approximately 9 weeks.

SAFEPAY:

As a software developer intern at Safepay, I had independently developed a tool using Python, SQL, and Docker that retrieved and processed transaction data stored in the company’s database. The tool was set up on Jupyter Notebook, so that the firm’s accounting department could easily run the whole script and retrieve the required data.

The main challenge for the project was to successfully and securely link the notebook with the firm’s MySQL database in the back-end. This mandated containerizing the Jupyter Notebook application via Docker and run its image alongside the back-end database’s MySQL image in one single container.

This tool facilitated the firm with settling payments, tracking transactions, and running analytics for operational strategies and growth analysis. Overall, through this tool I was able to raise Safepay’s efficiency by 70% in processing data from its MySQL database.

Bachelor of Science, Computer Science

University of Toronto

CGPA: 3.72