The Role
You will take ownership of the video-analysis pipeline and auto-tagging engine—the high-throughput indexing backbone that powers the product’s core intelligence. This is pure backend engineering at peak scale: managing 30+ microservices operating in elastic Kubernetes clusters, optimizing high-performance compute workloads under severe traffic spikes, and integrating production ML models directly into the pipeline.
About the Product
The product is an AI-powered sports media platform used by major sports organizations to transform live broadcasts into personalized digital content at scale. Every live stream is continuously ingested, analyzed, and enriched with AI-generated metadata, enabling automatic detection of key moments, intelligent video indexing, and near real-time content creation.
Behind the scenes, the platform processes massive volumes of live and archived video 24/7, powering personalized fan experiences across web, mobile, social, and broadcast channels. The backend infrastructure is built for extreme throughput, low-latency processing, and elastic scaling, combining distributed microservices with production-grade ML inference pipelines to deliver reliable performance under unpredictable traffic peaks.
Technology Stack: The infrastructure runs on cloud microservices deployed to Kubernetes, leveraging pub-sub architectures built with Kafka or Azure ServiceBus for real-time event routing. Storage and data access rely on relational SQL databases optimized for high-throughput, low-latency querying. Development workflows embrace modern tooling like Git and CI/CD pipelines, alongside AI-assisted tools such as Claude and Cursor integrated into the daily delivery cycle.
What You’ll Be Doing
- Own the video-analysis pipeline and auto-tagging engine, driving end-to-end performance from architectural design to production operation
- Integrate ML and AI inference models into backend services to handle automated real-time video indexing
- Redesign backend microservices and APIs to improve throughput, latency, and resource utilization during peak traffic surges
- Implement pub-sub event architectures using Kafka or ServiceBus to process video streams asynchronously
- Establish system observability, runbooks, and incident response tooling to ensure 24/7 uptime for critical backend services
- Mentor backend engineers, drive code review standards, and lead technical design across 30+ active microservices
What We Expect
Must-have
- 5+ years of experience building and scaling high-throughput backend systems in cloud/SaaS environments
- Proven expertise in a mainstream backend language (such as C#, Java, or Python) with strong distributed systems principles
- Hands-on experience designing relational SQL schemas and optimizing complex data access patterns
- Practical experience with pub-sub and event-driven messaging systems (Kafka, Azure ServiceBus, or equivalent)
- Track record of leveraging CI/CD pipelines and AI-assisted engineering tools (Claude, Cursor) in daily workflows
- Degree in CS/Software Engineering or equivalent real-world depth
Nice-to-have
- Production experience with .NET Core within a large microservices estate
- Deep cloud platform experience (Azure hyperscaler, networking, cost/performance trade-offs)
- Background handling asynchronous batch processing or high-scale ML model deployment pipelines
Why This Role Is Worth Your Time
- Direct ownership of pure backend compute at scale—no full-stack dilution, focusing entirely on high-load microservices, indexing engines, and real-time processing
- Deep integration with AI/ML engineering—you are building the actual infrastructure that serves, monitors, and runs machine learning inference models on live data
- High-autonomy environment with modern workflows where AI tools (Claude, Cursor) are embraced to eliminate boilerplate and keep focus on architecture
