Deep Learning Engineer

Responsibilities

• Design, implement, and optimize deep learning models (CNNs, RNNs, Transformers, etc.) to address project-specific challenges.
• Work with large-scale datasets, including data cleaning, preprocessing, augmentation, and feature engineering.
• Develop end-to-end pipelines from data ingestion through model training, validation, and deployment.
• Collaborate with data scientists, software engineers, and domain experts to integrate AI solutions.
• Conduct experiments, analyze results, and iterate to improve model performance.
• Ensure code quality with version control, documentation, and testing best practices.
• Stay up-to-date with the latest research and tools in deep learning and AI.
• Support model deployment on cloud platforms or edge devices as required.

Required Skills & Experience

• Proven experience in building and deploying deep learning models for real-world applications.
• Proficiency in Python and deep learning frameworks such as TensorFlow, PyTorch, or Keras.
• Strong understanding of neural network architectures (CNNs, RNNs, LSTMs, Transformers).
• Experience with data preprocessing techniques and working with unstructured data (images, text, audio).
• Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) is a plus.
• Solid knowledge of machine learning fundamentals and optimization techniques.
• Ability to work independently, manage time effectively, and communicate clearly with stakeholders.
• Fluency in English; knowledge of French or Dutch is an advantage.

Preferred Qualifications

• MSc or PhD in Computer Science, AI, Machine Learning, or a related field.
• Experience with MLOps tools and workflows.
• Knowledge of NLP, computer vision, or reinforcement learning is a bonus.
• Previous freelance or consulting experience is a plus.

What We Offer