The Indexing layer processes live sports broadcasts to produce AI-generated highlights in real time — and the video-analysis pipeline behind it is yours to build and operate. The core challenge isn't just shipping code; it's managing high-performance compute workloads across 30+ event-driven microservices that must auto-scale under extreme live-event traffic spikes without failing.
The platform transforms live sports broadcasts into personalized, publication-ready highlight packages while games are actively in progress. Operating at continuous scale, it automatically ingests, analyzes, and tags video streams to identify key moments. The system processes massive data volumes with zero tolerance for latency, directly powering downstream consumer-facing media products.
Technology Stack: The backend core is built on C# and .NET, running distributed microservices orchestrated via Kubernetes in cloud environments like Azure. Asynchronous data flows and event-driven architecture rely on pub/sub messaging infrastructure (Kafka and Azure Service Bus) with relational SQL databases for persistent storage. The engineering team actively leverages modern AI tooling, including Claude Code, Cursor, custom skills, and sub-agents, directly within their delivery workflows.
Architect and operate low-latency, event-driven microservices that handle live event traffic bursts with zero downtime
Bridge ML models into production by building resilient deployment pipelines that handle model drift, context limits, and non-deterministic outputs
Own end-to-end service delivery from initial RFC design through zero-downtime deployment and post-launch telemetry
Optimize pub/sub event pipelines across Kafka and Azure Service Bus to decrease processing latency and boost data throughput
Debug distributed performance bottlenecks and scale Kubernetes compute clusters handling concurrent 24/7 media processing
Collaborate with ML Engineers and Data Scientists to design systems that degrade gracefully during inference failures
4+ years scaling distributed microservices and backend systems in production cloud environments
Strong expertise in C# / .NET for high-throughput, low-latency application development
Production experience with Kubernetes, relational schema design, SQL query performance tuning, and messaging infrastructure (Kafka, Azure Service Bus)
Hands-on integration of AI-assisted engineering tools (Cursor, Claude Code, sub-agents) into daily workflow
Operational knowledge of production ML/AI behavior (inference performance, context limits, non-determinism)
Azure cloud infrastructure experience (networking, IAM, cost/performance optimizations) or exposure to video/media processing pipelines
Direct ownership of high-concurrency event pipelines where code changes instantly impact live, broadcast-level media products
Pragmatic adoption of AI engineering practices — AI coding assistants and sub-agents are integrated into production workflows rather than treated as an afterthought
Exposure to complex distributed systems engineering, balancing massive scale, live media ingestion, and operational cost efficiency
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