Principal LLM Engineer

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ReSpo.Vision is an AI and Computer Vision company transforming how sports are analyzed, visualized, and monetized. Our proprietary single-camera system extracts elite-level tracking data and performance analytics from standard broadcast or tactical video, without wearables or in-venue installations. Already used by global clubs, federations like FIFA, Concacaf, or CONMEBOL and for competitions like Euro or Copa America, we are actively expanding into media, fan engagement, and betting applications.

Our pipeline combines advanced computer vision and deep learning models to track all players and the ball in 3D using a single-camera feed. The resulting positional data powers our growing product suite : from match analytics platform to visual content, including 3D match reconstructions (see an early demo : YouTube ) and real-time broadcast augmentation with dynamic stats and virtual overlays. The system is built for scalability, leveraging cloud-native infrastructure, GPU inference pipelines, and sports-specific post-processing modules that turn raw detections into meaningful insights.

We’re now entering an exciting phase, pushing our tracking system toward real-time applications, lowering latency to enable live insights and instant visual augmentations. In parallel, we’re building new layers on top of our core data, including realistic 3D match reconstructions and virtual overlays for enhanced broadcast experiences.

(Your Role)

As a Principal ML Engineer specializing in LLMs at ReSpo.Vision, you'll build intelligent systems that transform how we understand and narrate sports. This is a hands‑on position where you'll integrate cutting‑edge language models with our sports tracking data, implement production LLM pipelines, and create AI‑powered features that enhance our analytics and content generation capabilities. You'll own LLM‑based initiatives from conception through deployment, personally coding at the intersection of NLP, sports data, and real‑time systems.

(You will be responsible for)

• LLM System Architecture : Design and implement production LLM pipelines that process our tracking data into natural language insights, automated commentary, and intelligent analytics
• Sports Intelligence Platform : Build systems that combine our computer vision outputs with LLMs to generate match summaries, player insights, tactical analysis, and real‑time commentary
• Retrieval & Context Systems : Develop RAG architectures that leverage our vast sports database, enabling LLMs to provide accurate, context‑aware responses about matches, players, and tactics
• Multi‑Modal Integration : Connect our visual tracking data with language models - turning player movements into narratives, tactical patterns into insights, and match events into engaging content
• Real‑Time LLM Infrastructure : Build low‑latency systems for live match narration, instant stat explanations, and interactive fan experiences powered by LLMs
• Prompt Engineering & Fine‑tuning : Optimize LLMs for sports‑specific tasks through prompt engineering, fine‑tuning, and potentially training sport‑specific models
• API & Integration Layer : Create robust APIs that allow our clients to leverage LLM capabilities, from automated match reports to conversational sports analytics

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