- Company Name
- Mustard Systems Ltd.
- Job Title
- Computer Vision Engineer
- Job Description
-
**Job Title**
Senior Computer Vision Engineer – Sports Analytics (Cricket)
**Role Summary**
Design, develop, and deploy production‑grade computer vision systems that extract actionable data from live and archival cricket video. Lead technical strategy, establish best practices, and collaborate across data science, domain experts, and engineering teams to automate and scale the entire analytics pipeline.
**Expectations**
- Lead end‑to‑end CV solution architecture for cricket video analytics.
- Own technical decisions, deliver high‑quality models, and iteratively improve accuracy and efficiency.
- Work autonomously while coordinating with cross‑functional stakeholders to align on data definitions, rules, and edge cases.
**Key Responsibilities**
- Build and tune object detection, tracking, pose estimation, action recognition, and temporal event‑detection models for cricket broadcasts and replays.
- Develop robust pipelines that handle variable camera angles, inconsistent framing, and broadcast‑quality footage.
- Implement replay‑handling logic to avoid duplicate or false event capture.
- Create data‑validation frameworks that compare automatically extracted outputs with manually curated ground truth.
- Design metrics, tests, and tooling to ensure data quality, reliability, and confidence.
- Scale solutions to process large archival footage volumes while maintaining performance and consistency.
- Identify and implement automation opportunities to reduce manual effort across the analytics workflow.
- Mentor junior engineers and influence future CV direction within the organization.
**Required Skills**
- Proven expertise in deploying production computer vision systems (object detection, tracking, pose estimation, action recognition, or temporal event detection).
- Strong programming skills in Python, TensorFlow/PyTorch, OpenCV, and related CV libraries.
- Experience with model serving, GPU optimization, and scaling pipelines to handle high‑volume video streams.
- Familiarity with video processing challenges: variable angles, compression artifacts, motion blur, and incomplete coverage.
- Ability to design validation frameworks, performance metrics, and automated testing for model outputs.
- Excellent problem‑solving, research‑driven yet pragmatic mindset balancing experimentation and delivery.
- Effective communication skills to collaborate with data scientists, domain experts, and engineers.
- Self‑motivated, able to lead technical discussions and make independent decisions.
**Required Education & Certifications**
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related technical field.
- Optional certifications: Deep Learning specialization, CV/ML professional courses, or relevant cloud‑platform training.