What you will do:
Develop and improve computer vision and deep learning models for production applications
Work on detection, tracking, and re-identification related tasks
Build reliable evaluation, benchmarking, and validation workflows
Optimise model performance for real-world deployment environments
Collaborate with backend, platform, and product teams to integrate AI capabilities into production systems
Analyse model performance, troubleshoot issues, and improve system reliability
Contribute to tooling, experimentation pipelines, and technical decision-making
Support continuous iteration through data-driven experimentation and validation
What you will need:
Strong Python development skills with solid software engineering practices
Hands-on experience with deep learning and computer vision
Practical experience in areas such as:
Object Detection
Multi-object Tracking
Re-Identification
Strong understanding of model evaluation methodologies and CV metrics
Experience exporting and validating models using ONNX or similar frameworks
Comfortable troubleshooting model, data, or inference-related issues
Ability to work across both ML research and engineering implementation
Comfortable using AI-assisted development tools effectively
Good English communication skills for collaboration with international teams
Nice-to-haves:
Experience with Vision-Language Models (VLMs)
Video-based or multi-camera AI systems
OpenVINO optimisation and quantisation (FP16 / INT8)
Real-world AI deployment experience in industries such as retail, manufacturing, or smart environments
Experience maintaining ML evaluation or benchmarking pipelines