- Developed a scalable SaaS platform tailored for football athletes and coaches, integrating advanced video analytics for performance enhancement, achieving an increase in data processing speed for real-time feedback.

- Engineered a cost-effective custom video storage solution on Bare Metal Server with optimized streaming services, cutting latency and storage expenses by over 40%, supporting 10,000+ concurrent streams.

- Implemented a real-time communication system using Node.js and Socket.IO, facilitating instant messaging among stakeholders, reducing message delivery times by 50%.

- Spearheaded the integration of automated testing (Jest for React, Django’s testing framework) and continuous integration, enhancing code reliability.

- Developed a scalable microservices architecture leveraging Docker and Kubernetes, optimizing module maintenance and scalability for robust application performance.

- Implemented advanced multi-tier caching using Redis, significantly reducing response times and server load by 35% during peak demand.

- Engineered efficient data management solutions with MongoDB, designing optimized schemas that expedited complex query execution for enhanced analytics and reporting.

- Constructed a sophisticated analytics module in Python and Django, processing video data to deliver actionable insights into player performance.

- Designed a user-centric interface with React, improving user engagement through intuitive navigation and standard UI/UX principles.

- Applied rigorous refactoring and adopted standard engineering design patterns, increasing development velocity and system robustness.

- Enabled robust real-time data synchronization using WebSockets, ensuring immediate updates and notifications across client-server communications.

- Led a dynamic team of developers, promoting a culture of collaboration and continuous learning, and mentoring in advanced software engineering methodologies.

- Developed an AI-driven SaaS platform for football performance analytics, utilizing machine learning to enhance athlete progression towards NFL/NCAA levels, achieving a 20% increase in predictive accuracy for performance outcomes.

- Architected a real-time video analysis system using TensorFlow and OpenCV to assess gameplay, providing actionable insights through AI-based play detection and performance metrics.

- Led the integration of a predictive modeling system using Python’s Scikit-Learn and TensorFlow, which accurately forecasted team performance by analyzing practice and game data.

- Designed and implemented a personalized AI recommendation engine to guide athletes on performance improvement, using advanced machine learning algorithms to tailor feedback.

- Engineered a microservices architecture optimized for AI and application operations, deploying Docker and Kubernetes for effective component management, resulting in a 30% improvement in system scalability.

- Implemented a multi-level caching strategy with Redis, significantly enhancing data retrieval speed and reducing latency for live video analysis.

- Leveraged MongoDB for high-throughput data storage and complex query execution, ensuring robust real-time analytics and handling of large-scale datasets.

- Developed secure, high-performance API endpoints using Node.js, enabling efficient data exchange between frontend applications and backend AI services.

- Applied convolutional neural networks (CNNs) for critical video frame analysis and feature extraction, improving play-to-play detection accuracy.

- Created a responsive and intuitive user interface with React, optimized for desktop and mobile platforms, enhancing user engagement and accessibility.

- Automated deployment processes and ensured system resilience using CI/CD pipelines, achieving a 99.9% uptime and reducing deployment cycles.

- Conducted rigorous data preprocessing and augmentation to enhance model training, reducing overfitting while increasing accuracy across diverse gameplay scenarios.