Key Responsibilities
* Develop, 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 & Experience
* MSc/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 Have
* Experience 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.