Senior Python Engineer

As a Principal Software Engineer, you will shape and build the core backend systems that operationalise our computer vision models at scale. This is a hands-on, high-impact position, expect to be writing production code daily while also setting technical direction through what you build. Your work will span both large-scale batch systems and real-time streaming pipelines that power advanced media and sports data products. [Responsibilities] Platform Architecture & Strategy: Design and build the full-stack infrastructure for data from video ingestion through inference pipelines to product delivery layers Scalable Systems Development: Engineer systems for two extremes batch processing at massive scale and low-latency pipelines for live match augmentations Product-Technical Leadership: Translate product goals into robust technical architecture; drive major projects from conception to deployment Real-Time Infrastructure: Architect ultra-low-latency streaming components that enable live data overlays and in-broadcast enhancements ML Platform Engineering: Develop infrastructure to support model deployment at scale, including GPU orchestration, optimized inference, and scalable serving Initiative Ownership: Take end-to-end responsibility for complex, ambiguous technical challenges that unlock new product features Distributed Systems Engineering: Build robust, cloud-native platforms designed for high availability, performance, and scale [Who You Are] 5+ years building production-grade systems, with a focus on distributed architectures Experience with both large-scale batch processing and real-time streaming pipelines Python is your core language, but you're comfortable choosing the right tool for the job Cloud-native thinking, comfortable with concepts like autoscaling, queues, SLAs Deep familiarity with ML system challenges: GPU orchestration, inference performance, stateful services Strong cross-functional communication, you can move fluidly between product conversations and technical deep-dives You've scaled systems before and understand trade-offs between performance tuning and shipping fast [Core Technical Requirements] Expertise in distributed system design and implementation Proven track record with both batch and real-time processing systems Deep experience with cloud infrastructure and container orchestration (Kubernetes, Docker) Production experience with high-throughput, low-latency system architecture Familiarity with ML ops workflows: serving models, managing GPUs, building feature stores Hands-on experience with infrastructure-as-code and modern DevOps practices Comfortable using AI-assisted development tools (e.g., Copilot, ChatGPT, Cursor, Claude) [Nice to Have] Experience working with video streaming, processing, or broadcast systems Background in real-time media tech or interactive sports/entertainment products Understanding of computer vision pipelines and deep learning workflows Contributions to open-source infrastructure or systems projects Experience scaling platforms to support hundreds or thousands of concurrent users or compute processes