Machine Learning Engineer - Computer VisionExplore Group • London, England, United Kingdom
Machine Learning Engineer - Computer Vision
Explore Group
- London, England, United Kingdom
- London, England, United Kingdom
Über
The Explore Group are working with one of our tier 1 clients in securing an experienced Machine Learning Engineer with strong expertise in Computer Vision to join our team on a 6 month initial contract (with guaranteed extensions). You will play a key role in designing, building, and deploying machine learning models that drive innovation in image and video processing.
Responsibilities- Develop, train, and optimise computer vision models for real world applications.
- Work with large datasets including images and video, ensuring data preprocessing, augmentation, and pipeline optimisation.
- Deploy ML models into production, ensuring scalability, efficiency, and reliability.
- Collaborate closely with data scientists, software engineers, and product teams to translate business needs into technical solutions.
- Stay up to date with the latest advancements in deep learning, computer vision, and related technologies.
- Strong hands on experience with Computer Vision frameworks (e.g., OpenCV, PyTorch, TensorFlow).
- Proficiency in Python and ML/DL libraries (NumPy, Pandas, scikit-learn).
- Proven track record of building and deploying ML models in production environments.
- Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar).
- Experience working with cloud platforms (AWS, Azure, or GCP).
- Solid understanding of CNNs, object detection, segmentation, and image classification.
- Strong problem-solving skills and ability to work in a hybrid, collaborative environment.
- Experience with transformer-based vision models (ViT, CLIP, SAM).
- Familiarity with real-time inference and optimisation (ONNX, TensorRT).
- Previous work on video analytics, 3D vision, or multi-modal ML projects.
- Mid-Senior level
- Contract
- Consulting
- Staffing and Recruiting
Sprachkenntnisse
- English
Hinweis für Nutzer
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