AI Computer Vision Engineer

About the role

We are looking for a highly driven and hands-on Computer Vision AI Engineer who thrives on taking full ownership of the machine learning lifecycle. In this role, you won't just be tweaking existing architectures; you will be responsible for everything from raw data collection and annotation pipeline management to training, optimising, and deploying real-time deep learning models for Smart City and traffic monitoring applications. You will collaborate closely with a multi-disciplinary technical team—including Senior CV Engineers, Client Full Stack Developers, and Special Projects & Integration experts—to build and scale state-of-the-art AI systems that operate efficiently in production.

Key Responsibilities:

• End-to-End Model Development: Design, train, and deploy deep learning models focused on computer vision tasks (e.g., object detection, classification, ANPR) for Smart City projects.

• Data Pipeline Ownership: Lead the data collection, curation, and labelling processes. Ensure datasets are high-quality, diverse, and free of bias to train robust models.

• Optimisation & Deployment: Translate trained models into production-ready solutions. Optimise models for inference speed and accuracy in real-world, high-traffic environments.

• Cross-Functional Collaboration: Work seamlessly with software engineers and integration leads to embed AI models into larger system architectures, including C++ applications and MSSQL database environments.

• Performance Monitoring: Continuously monitor, analyse, and troubleshoot AI model performance in production, iterating quickly to resolve edge cases and improve metrics.

What we are looking for

• Bachelor’s or Master’s degree in Computer Science, Electronics Engineering, AI, or a related quantitative field.

• Strong proficiency in Python and solid experience with at least one major deep learning framework (PyTorch or TensorFlow).

• Deep understanding of core computer vision techniques, algorithms, and libraries (e.g., OpenCV).

• Experience with deployment and optimisation tools (e.g., TensorRT, ONNX) and a good understanding of integrating models with C++ codebases and databases (like MSSQL).

• Proven track record of managing datasets and understanding the nuances of data preprocessing and augmentation.

Soft Skills:

• Proactive Problem Solver: A "get-it-done" attitude with the ability to troubleshoot complex issues in production environments independently.

• Strong Communicator: Excellent ability to articulate technical challenges and solutions clearly within a fast-paced team environment.

• Detail-Oriented: A rigorous approach to model evaluation metrics, code quality, and documentation.