Computer Vision Lead
First seen by Alion on Jul 30, 2026.
Role Overview :
We are looking for a Computer Vision Lead to head the vision and multimodal AI charter behind our next-generation sports AI products. This is a hands-on leadership role: you will own the technical direction of our real-time sports understanding stack, set the architecture and research agenda, and build and mentor a high-performing team of Computer Vision and AI Engineers. You will be accountable for taking models from research to reliable, low-latency production systems experienced by millions of fans.
Key Responsibility Areas :
1. Technical Leadership & Team Building :
- Own the technical vision, architecture, and roadmap for Computer Vision and multimodal AI across the product portfolio.
- Lead, mentor, and grow a team of Computer Vision and AI Engineers; drive hiring, onboarding, and capability development.
- Set engineering standards for experimentation, code quality, documentation, reproducibility, and model governance.
- Translate product goals into technical milestones, effort estimates, and delivery plans; own execution end to end.
- Make build-vs-buy and architecture trade-off decisions across model, infrastructure, and vendor choices.
2. AI Product Development & Language Intelligence :
- Define the strategy for AI-powered language products that enhance sports content creation and fan engagement.
- Lead development of automated live commentary systems using Large Language Models (LLMs), multimodal AI, and speech technologies.
- Architect intelligent pipelines that fuse vision, audio, and contextual match data into real-time insights and narratives.
- Evaluate and adopt emerging AI architectures to keep product capabilities ahead of the market.
3. Computer Vision & Model Development :
- Direct the design, training, and optimization of computer vision models for live sports analytics.
- Guide algorithm development for image and video understanding, including player, ball, and object tracking.
- Set architecture direction across CNNs, Vision Transformers (ViTs), YOLO, Faster R-CNN, Mask R-CNN, and vision-language models.
- Oversee object detection, image classification, segmentation, pose estimation, OCR, facial recognition, and event detection workstreams.
- Establish data strategy : annotation pipelines, augmentation, dataset quality, and data engineering best practices.
- Define evaluation frameworks and benchmarks for accuracy, latency, and robustness; drive continuous improvement against them.
4. Deployment, Performance Optimization & Production Engineering :
- Own the deployment architecture for models across cloud, edge devices, and GPU-enabled environments.
- Architect scalable inference pipelines with high throughput and low latency for real-time applications.
- Drive deployment practices using Docker, ONNX, TensorRT, FastAPI, Kubernetes, and edge GPU platforms.
- Ensure robust integration into production systems through APIs, microservices, and modern software engineering practices.
- Establish monitoring, observability, and MLOps practices for production reliability and cost efficiency.
5. Innovation, Collaboration & Research :
- Partner with Product, Design, Data Science, and Software Engineering leadership to shape the product roadmap.
- Lead applied research in Computer Vision, Multimodal AI, Generative AI, and Sports Analytics; identify what is worth productionizing.
- Represent the team in cross-functional and leadership forums; communicate technical trade-offs to non-technical stakeholders.
- Champion engineering excellence through architecture reviews, design reviews, and code reviews.
Skills
Computer Vision, Artificial Intelligence, LLM, Speech Recognition, R-CNN, Optical Character Recognition, Generative AI, MLOps, FastAPI