Senior AI Engineer in Computer Vision
Faktion BVover 2 years ago
Antwerp, BelgiumSenior
Responsibilities
- Develop, train, evaluate, deploy, and maintain deep learning models for computer vision tasks including object detection and image classification.
- Build and maintain data, training, and inference pipelines, primarily using Azure Machine Learning.
- Process and curate large image datasets, including multispectral and other multi-channel imagery.
- Investigate data quality, distribution shifts, labeling inconsistencies, model errors, and production performance issues.
- Define data collection, annotation, preprocessing, feature engineering, augmentation, validation, and evaluation strategies.
- Optimize models for latency, throughput, memory, and hardware constraints in real-time and production environments.
- Prototype new architectures and algorithms and integrate effective approaches into production systems.
- Improve shared ML platforms and tooling, including internal SDKs, data schemas, training pipelines, and deployment tooling.
- Review pull requests and maintain engineering, testing, documentation, and code quality standards.
- Collaborate with ML engineers, software engineers, data engineers, annotation teams, and customer teams.
- Stay current with developments in computer vision, deep learning, and MLOps.
Requirements
- Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field, or equivalent professional experience.
- Several years of professional experience building machine learning systems with a strong focus on computer vision and deep learning.
- Strong Python programming skills.
- Hands-on experience with PyTorch and/or TensorFlow.
- Strong understanding of object detection, image classification, model architectures, loss functions, augmentation, training techniques, and evaluation metrics.
- Experience with computer vision tooling such as OpenCV, YOLO-based architectures, or MMDetection.
- Experience building and debugging production machine learning pipelines and models.
- Experience with a cloud ML platform such as Azure Machine Learning, AWS SageMaker, or Google Vertex AI; Azure experience is a strong plus.
- Familiarity with Docker, automated testing, versioning, monitoring, and software engineering practices for production ML systems.
- Experience optimizing models for real-time or high-throughput inference, ideally on edge devices or production hardware.
- Strong analytical, problem-solving, communication, collaboration, and ownership skills.
- Nice-to-have experience with MLOps platforms, shared ML infrastructure, multispectral or hyperspectral imagery, embedded hardware, GPUs, industrial machines, model optimization, annotation workflows, scalable ML systems, technical mentoring, technical leadership, publications, or relevant research.
Benefits
- Salary package with additional perks including a company car and fuel card or a mobility budget.
- Comprehensive hospitalization and group insurance.
- Top-tier laptop and smartphone.
- Professional development opportunities and an innovation budget for educational open-source projects.
- Virtual team-building activities and team gatherings.
- Flexible hybrid working policy allowing employees to choose where, how, and when they work.
Categories