.NET Backend Developer (NXJ-195)

Newxel; Warszawa; On-site; PLN 260,000 - 380,000; Full time

Job summary

Newxel is seeking a seasoned backend engineer to design and operate a high-performance, event-driven microservices platform for live sports video analysis. You will work across the stack, from low-latency services to production ML deployment pipelines, ensuring reliability under extreme traffic.

Join a team leveraging Azure cloud, Kafka, and Azure Service Bus to power real-time AI-based highlights. The role demands strong C# / .NET expertise and an aptitude for 24/7 scalability in a modern cloud

Qualifications

• 4+ years of scaling distributed microservices and backend systems in production cloud environments.
• Strong expertise in C# / .NET for high-throughput, low-latency applications.
• Production experience with Kubernetes, SQL design, query performance tuning, and messaging systems (Kafka, Azure Service Bus).
• Hands-on integration of AI-assisted engineering tools into daily workflows.

Responsibilities

• Architect and operate low-latency, event-driven microservices that handle live event bursts with zero downtime.
• Bridge ML models into production with resilient deployment pipelines and drift handling.
• Own end-to-end service delivery from RFC design to zero-downtime deployment and telemetry.
• Optimize pub/sub pipelines to reduce latency and boost throughput (Kafka, Azure Service Bus).
• Debug distributed performance bottlenecks and scale Kubernetes compute for 24/7 media processing.
• Collaborate with ML engineers to design systems that degrade gracefully during inference failures.

Skills

Distributed microservices

C# / .NET

Kubernetes

Kafka

Azure Service Bus

SQL

Tools

Kafka

Azure Service Bus

Kubernetes

Job description

The Role

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.

About the Product

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.

What You’ll Be Doing

• 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

What We Expect

Must-have

• 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

Nice-to-have

• 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

Why This Role Is Worth Your Time

• 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