Senior Software Engineer with 4+ years of experience building scalable AI-powered platforms, distributed backend systems, and cloud-native applications across sports-tech and SaaS domains. Strong expertise in Python, Rea…
Senior Software Engineer with 4+ years of experience building scalable AI-powered platforms, distributed backend systems, and cloud-native applications across sports-tech and SaaS domains. Strong expertise in Python, React, TypeScript, Node.js, REST APIs, microservices, distributed systems, and modern AI technologies including LLMs, NLP, LangChain, RAG pipelines, and vector databases. Proven success delivering production-ready products across AWS, GCP, and Azure environments with a strong focus on scalability, performance optimization, and intelligent automation. Experienced in architecting high-performance backend systems, designing scalable APIs, optimizing cloud infrastructure, and developing AI-assisted workflows that drive revenue growth and operational efficiency. Passionate about solving complex engineering problems and building intelligent systems that scale globally.
Built Darkhorse’s AI-powered sports reel editing platform generating over $300K revenue within 90 days.
Architected scalable backend systems using Python, Node.js, Redis, and cloud-native infrastructure.
Developed AI-assisted clip generation workflows reducing athlete editing time by 70%.
Delivered advanced reel editing capabilities improving analyst productivity and platform engagement.
Led development of multi-sport compatibility reducing onboarding time from months to hours.
Managed integrations with Hudl, Veo, Trace, and PixelLot platforms.
Supported scalable infrastructure deployments across AWS, GCP, and Azure environments.
Reduced API response latency from 4 seconds to 200 milliseconds through Redis caching and pipeline optimizations.
Built scalable API processing workflows for high concurrent sports-video workloads.
Optimized distributed backend architecture improving reliability and large-scale processing efficiency.
Collaborated with engineering, product, and leadership teams in Agile environments.
Contributed to scalable SaaS architecture, feature planning, and production deployments.
Built Darkhorse’s AI-powered sports reel editing platform generating over $300K revenue within 90 days., Architected scalable backend systems using Python, Node.js, Redis, and cloud-native infrastructure., Developed AI-assisted clip generation workflows reducing athlete editing time by 70%., Delivered advanced reel editing capabilities improving analyst productivity and platform engagement.
Developed scalable SaaS applications using React.js, Angular, and TypeScript., Built secure and high-performance RESTful APIs using Node.js and Express.js., Improved frontend responsiveness and backend scalability across production applications., Contributed to SQL Server to PostgreSQL migration improving scalability and data reliability., Worked on debugging, production issue resolution, and performance optimization initiatives., Collaborated with QA, product, and engineering teams to deliver production-ready features.
Developed scalable SaaS applications using React.js, Angular, and TypeScript.
Built secure and high-performance RESTful APIs using Node.js and Express.js.
Improved frontend responsiveness and backend scalability across production applications.
Contributed to SQL Server to PostgreSQL migration improving scalability and data reliability.
Worked on debugging, production issue resolution, and performance optimization initiatives.
Collaborated with QA, product, and engineering teams to deliver production-ready features.
We are building an AI-powered sports video platform mainly for youth soccer communities including players, coaches, managers, and academies.In this platform, users upload match videos. Our system stores the original MP4 video and also sends it to a video-processing server.There we use FFmpeg to generate HLS streaming files and thumbnails for optimized playback.We also generate highlights/clips based on timestamps like display start time and end time. Using HLS chunks and stored timestamps, we dynamically create reels and highlights.We also support third-party integrations like Hudl or Veo where videos come from external platforms. Even if they provide CDN URLs, our system first downloads and processes the video internally for consistency in workflows.My contribution was mainly on backend development including APIs, video workflows, integrations, performance optimization, caching, and AI-related features.
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