Senior Computer Vision Engineer

This is a hands-on AI engineering role focused on building computer vision systems that operate reliably in real-world environments. You will work on detection, tracking, recognition, behaviour understanding and visual intelligence models used across security, monitoring and automation applications.

You will work closely with our AI, software and platform engineering teams to take models from experimentation through validation and into production customer deployments.

Key Responsibilities

• Design, train, evaluate and improve computer vision and deep learning models for security applications.
• Develop models for applications such as:
• Person and vehicle detection
• Object detection and classification
• Multi-object tracking
• Face detection and recognition
• Vehicle and number plate recognition
• Intrusion and perimeter monitoring
• Anomaly and unusual-behaviour detection
• Activity and event recognition
• Occupancy and movement analysis
• Visual scene understanding
• Develop robust models that work across different cameras, lighting conditions, environments and viewing angles.
• Improve model accuracy, precision, recall and reliability while reducing false positives and false negatives.
• Build and curate datasets for training, validation and benchmarking.
• Design data annotation and dataset-quality processes.
• Fine-tune and optimise existing models for Avalon-specific use cases.
• Research and evaluate new computer vision architectures, models and techniques.
• Develop multi-camera and temporal computer vision approaches where required.
• Improve object identity and tracking across frames and camera events.
• Develop algorithms for combining information across multiple frames rather than relying only on individual images.
• Evaluate model performance using real-world customer footage and representative test datasets.
• Investigate difficult edge cases and systematically improve model behaviour.
• Optimise models for GPU, edge and production inference environments.
• Improve inference speed, memory utilisation and computational efficiency.
• Work with technologies such as ONNX, TensorRT or similar inference optimisation frameworks where appropriate.
• Integrate trained models into production applications and computer vision pipelines.
• Work with Platform Engineers to deploy and monitor models in customer environments.
• Create automated model evaluation and regression-testing processes.
• Monitor production model performance and identify model drift or performance degradation.
• Document experiments, datasets, model versions and evaluation results.
• Contribute to the architecture and technical direction of Avalon’s computer vision platform.

We are looking for someone with practical experience in computer vision, machine learning or deep learning, preferably with experience taking models beyond experimentation and into real-world applications.

You should have experience with some of the following:

• Python
• PyTorch or TensorFlow
• OpenCV
• Object detection models such as YOLO, RT-DETR, Faster R-CNN or similar
• Image classification and segmentation
• Multi-object tracking
• Deep learning model training and fine-tuning
• Dataset preparation and augmentation
• Computer vision evaluation metrics
• GPU-based model training and inference
• ONNX or TensorRT
• Linux
• Git
• Docker

Experience with the following would be particularly beneficial:

• CCTV or surveillance computer vision
• Real-time video analytics
• Face recognition
• Automatic Number Plate Recognition (ANPR/ALPR)
• Person re-identification
• Vehicle re-identification
• Multi-camera tracking
• Behaviour or activity recognition
• Vision Transformers
• Video understanding models
• Edge AI
• NVIDIA GPUs and CUDA
• Model quantisation and inference optimisation
• Production ML/MLOps systems

We do not expect candidates to have experience with every technology listed.

The Ideal Candidate

You will be someone who:

• Enjoys solving difficult real-world computer vision problems rather than working only with clean benchmark datasets.
• Understands that a model performing well in a laboratory does not automatically mean it will perform reliably in production.
• Can analyse false positives, false negatives and difficult edge cases and determine why they occur.
• Thinks carefully about dataset quality and understands how strongly data affects model performance.
• Can evaluate models objectively using measurable evidence.
• Is comfortable experimenting with new architectures and research techniques.
• Can balance model accuracy with inference speed and computational requirements.
• Understands the challenges of video analytics including occlusion, lighting variation, camera movement, perspective and low-quality imagery.
• Has a strong engineering mindset and can turn research ideas into maintainable production systems.
• Can work independently while collaborating closely with software, platform and product teams.

At Avalon, computer vision is a core part of our intelligent security technology.

Rather than simply running standard object detection models, we are developing systems that can understand what is happening across a security environment, correlate information over time and determine which events actually require attention.

You may work on problems such as distinguishing genuine security events from normal activity, maintaining the identity of people and vehicles over time, improving recognition under difficult camera conditions, reducing duplicate security events, combining information from multiple frames and improving the reliability of AI decisions before they are presented to customers.

The role therefore combines computer vision research, applied AI engineering and real-world production deployment.

About Avalon

Avalon Artificial Intelligence Limited develops intelligent security and automation technologies.

Our Avalon Sentinel Suite combines AI-powered video and image analytics, face recognition, access-control intelligence, anomaly detection, intrusion monitoring, behavioural and event intelligence, non-invasive sensing and operational intelligence within a configurable security platform.

As a Computer Vision Engineer, you will help develop the intelligence behind these systems and contribute directly to the models and algorithms used in real customer environments.