.NET Backend Developer (NXJ-195)

We turn a live sports broadcast into personalized, ready-to-publish highlights automatically and at a scale no human editing team could match. The team owns the Indexing layer: the video-analysis pipeline and tagging engine that runs high-performance compute workloads on Kubernetes and spans 30+ backend microservices in an event-driven orchestration system. The system runs 24/7 with elastic scaling that can grow to very high capacity.

• Bachelor's degree in Computer Science, Software Engineering, or related field, or equivalent practical experience.
• 5+ years of experience building, scaling, and operating backend systems in production cloud environments.
• Proven ability to quickly understand and contribute to large existing systems.
• Strong skills in at least one mainstream backend language; expertise in C#/.NET preferred; strong Java, Python, or Go engineers also welcome.
• Real experience with distributed systems and microservices, including Kubernetes workloads.
• Hands-on experience with AI coding tools such as Claude Code or Cursor.
• Production experience with pub/sub and messaging systems.
• Proficient in SQL, relational schema design, and performance-minded querying.
• Methodical problem-solving approach with clear communication.
• Willingness to own a service after deployment and openness to adapting design based on feedback.

Nice to have:

• Understanding of AI and ML system behavior in production, including inference and evaluation.
• Experience with Azure at scale covering networking, identity, and performance tradeoffs.
• Experience in video, media, or ML-heavy pipelines.

• Design, build, and operate backend microservices and APIs at scale, prioritizing reliability, latency, cost, and operability.
• Own projects end to end: clarifying requirements, proposing approaches, shipping incrementally, and handling rollout, monitoring, and iteration.
• Collaborate with Data Scientists and ML Engineers to get models into production and design systems to handle model failure and drift.
• Build event-driven flows on pub/sub infrastructure such as Azure Service Bus and Kafka.
• Investigate throughput, latency, and stability issues under peak traffic and improve observability and runbooks based on findings.