May 2025 – Sep 2025 · 4 mos · United States · Remote

• Built and deployed end-to-end machine learning pipelines in Python for production, integrating REST APIs and scalable data orchestration for real-time inference.
• Improved model inference throughput by 22% using PyTorch quantization, mixed-precision (AMP), and GPU batch-size optimization on NVIDIA hardware.
• Designed performance benchmarking and profiling workflows using PyTorch Profiler and cProfile, identifying bottlenecks and implementing optimizations that reduced latency by 18%.
• Deployed models on cloud-based GPU infrastructure and automated experiments using MLflow for reproducible training, tracking, and hyperparameter management.

Nov 2024 – Apr 2025 · 5 mos · New York, United States · Remote

• Collaborating with the development team to design and implement AI-driven features for the Musync app.
• Assisting in the development and maintenance of software applications, ensuring they meet user needs and performance standards.
• Participating in code reviews, testing, and debugging to improve software quality and reliability.
• Working closely with developers to understand project requirements and deliver high-quality solutions.
• Supporting the protection of confidential information and the intellectual property portfolio of Musync, LLC.

Jun 2021 – Aug 2021 · 2 mos

• Reduced error rates by 15% through the implementation of data validation protocols, enhancing product quality and customer satisfaction.
• Increased data processing efficiency by 20% by developing a real-time digital gauge recognition system, reducing production downtime and costs.
• Optimized deep learning algorithms to achieve an 88% accuracy rate, improving predictive maintenance and operational efficiency.
• Leveraged advanced Python libraries for data manipulation and analysis, which streamlined the data pipeline and enhanced decision-making processes.
• Collaborated effectively with cross-functional teams to drive project success, leading to improved team synergy and project outcomes.

Oct 2020 – Dec 2020 · 2 mos

• Engineered a Neural Network using the MNIST Dataset for real-time digit recognition, resulting in a highly accurate and responsive application for handwriting recognition.
• Implemented a Convolutional Neural Network (CNN) with transfer learning techniques for image classification, achieving high accuracy and reducing model training time.
• Demonstrated proficiency in Python libraries, including TensorFlow, Keras, OpenCV, and NumPy, contributing to the successful deployment of machine learning models in production environments.

May 2019 – Jun 2019 · 1 mo

• Developed a Hangman game using Python, highlighting strong programming and algorithmic problem-solving skills, and enhancing user engagement through an interactive experience.
• Conducted literature reviews and technical report writing under the guidance of a postgraduate student, contributing to the academic research and development of new methodologies in Computer Science.