Canada
Currently pursuing a Master of Applied Computing at the University of Windsor, with a strong foundation in software engineering and advanced computational techniques. Recently completed an AI Developer Co-op at JLR, where the team contributed to artificial intelligence and deep learning projects within a confidential R&D environment. Designed and implemented scalable AI/ML pipelines using Python while adhering to best practices for performance and reproducibility. Collaborated on cross-functional teams under strict confidentiality protocols, applying deep learning techniques to tackle real-world engineering challenges. Dedicated to advancing skills in AI, low-code development, and cloud-based solutions.
Completed an AI Developer Co-op internship contributing to industry-grade artificial intelligence and deep learning initiatives in a confidential research and development environment Designed and implemented AI/ML pipelines using Python, following best practices for scalability, reproducibility, and performance Applied deep learning techniques and data-driven analysis to solve complex, real-world engineering problems Collaborated in a remote, cross-functional team, adhering to strict NDA, data governance, and professional engineering standards Conducted experimentation, evaluation, and validation of AI models to ensure correctness and reliability Leveraged data visualization and analytical insights to support technical decision-making Strengthened expertise in applied AI, model development, and production-oriented workflows within an enterprise setting
Engineering CI/CD and release workflows for reliable application deployment and faster iteration cycles. Developing scripts, feature fixes, debugging solutions, and internal engineering utilities. Performing software validation, regression assurance, defect analysis, and release quality checks. Supporting Linux/cloud deployment environments, monitoring systems, and production issue resolution. Utilizing Generative AI and LLM-powered development assistants to accelerate code generation, troubleshooting, technical documentation, and test design activities. Driving workflow automation across development, testing, and deployment tasks to improve team productivity. Collaborating across the complete software delivery lifecycle from development through deployment and post-release stabilization.