Key ResponsibilitiesDevelop, train, and evaluate deep neural networks for tasks such as (e.g., image recognition, NLP, speech, time-series forecasting).Optimize models for performance, accuracy, and deployment constraints (e.g., latency, memory, throughput).Implement model training pipelines using frameworks such as PyTorch or TensorFlow.Collaborate with data engineers and domain experts to prepare and preprocess large-scale datasets.Contribute to model deployment using tools like ONNX, TensorRT, or cloud-native solutions (AWS, Azure, GCP).Stay up-to-date with the latest advancements in deep learning research and techniques. Required Skills & ExperienceMSc/PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.3+ years of hands-on experience in deep learning.Proficient in Python and deep learning frameworks (e.g., PyTorch, TensorFlow, Keras).Solid understanding of neural network architectures (CNNs, RNNs, Transformers, GANs, etc.).Experience with data pipeline tools (e.g., Pandas, Dask, Airflow) and model versioning (MLflow, Weights & Biases).Strong knowledge of software engineering practices: Git, Docker, CI/CD.Comfortable working in a Linux-based environment. Nice to HaveExperience with edge AI or model quantization/pruning.Knowledge of MLOps principles and deployment on Kubernetes.Familiarity with regulatory or ethical aspects of AI in Europe (e.g., GDPR, AI Act).Previous experience in a freelance/consulting capacity in Belgium.