The Tech team at SportsAI leverages Computer Vision and Deep Learning to transform raw match footage into real-time analytics, automated highlights, and AI-driven player ratings.
Our stack is designed for high-performance compute and scalability: a Java backend supported by SQL/NoSQL datastores, React Native for cross-platform delivery, and a hybrid AWS/GCP infrastructure to power intensive workloads like the Ray framework.
We architect complex solutions at the intersection of Video Engineering, AI, and Product.
Your Role:
Own end-to-end responsibility of design, architecture, development, deployment & monitoring phases
Work on latest technologies to deploy AI/ML infra such as Temporal and Ray to scale complex, human-in-the-loop, retry logics in video processing workflows
Bridge the gap between Product and AI, integrating asynchronous AI/ML pipelines and GenAI models (GCP Vertex AI) into the real-time user experience
Understand and solve real business needs at a large scale by applying engineering and analytical problem-solving skills
Develop and own features working in a distributed microservices environment
Mentor engineers in the team and ensure standards are maintained at every level
Qualifiers:
6+ years of hands-on experience in any typed language, preferably Java and Python
Working experience of GCP or any cloud stack
Proven ability to build asynchronous, event-driven systems
Additional Information
Dream Sports is India’s leading sports technology company with 280 million+ users, housing brands such as Dream11, the world’s largest fantasy sports platform, FanCode, a premier sports content & commerce platform and DreamSetGo, a sports experiences platform.
Dream Sports is based in Mumbai and has a workforce of close to 1,000 ‘Sportans’. Founded in 2008 by Harsh Jain and Bhavit Sheth, Dream Sports’ vision is to ‘Make Sports Better’ for fans through the confluence of sports and technology.
Checked out Dream Locker Room yet? Head over to our official blog to get a glimpse into our culture, and how we ‘Make Sports Better’, together.
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