Role overview
We are looking for a skilled and forward-thinking AI Engineer to design, develop, and deploy scalable artificial intelligence solutions that deliver measurable business impact. You will be responsible for building end-to-end machine learning systems, from data exploration and model development to production deployment and monitoring.
You will collaborate closely with Data Engineers, Data Scientists, Software Engineers, and Product Owners to transform business requirements into robust AI-driven applications and services.
What we're looking for
- Design, develop, train, and optimize machine learning and deep learning models for real-world applications.
- Build and maintain end-to-end ML pipelines, including data ingestion, preprocessing, feature engineering, model training, validation, and testing.
- Develop and expose AI-powered services and RESTful APIs for integration into production systems.
- Work with structured and unstructured datasets (large-scale datasets, time-series, text, images, etc.).
- Ensure model scalability, performance, robustness, and reliability in production environments.
- Deploy, version, and monitor models using MLOps best practices.
- Implement automated retraining, model validation, and performance tracking.
- Collaborate with cross-functional teams to translate business challenges into AI solutions.
- Document models, pipelines, and architecture decisions to ensure maintainability and governance.
- Contribute to continuous improvement of AI standards, best practices, and architecture.
Required Technologies & SkillsProgramming & Data
- Strong programming skills in Python and solid knowledge of SQL.
- Experience with data manipulation libraries such as Pandas and NumPy.
- Knowledge of software engineering best practices (clean code, modular design, testing).
Machine Learning & Deep Learning
- Hands-on experience with ML/DL frameworks:
- TensorFlow
- PyTorch
- scikit-learn
- Experience with model evaluation, hyperparameter tuning, and optimization techniques.
- Understanding of supervised, unsupervised, and potentially reinforcement learning methods.
- Experience with NLP, computer vision, or generative AI is a plus.
Cloud & Infrastructure
- Experience with cloud platforms: AWS, Azure, or Google Cloud Platform (GCP).
- Familiarity with cloud-based ML services (e.g., SageMaker, Azure ML, Vertex AI).
- Experience with distributed computing or big data tools (e.g., Spark) is an asset.
MLOps & Deployment
- Strong understanding of MLOps principles.
- Experience with:
- Docker and containerization
- Kubernetes for orchestration
- MLflow or similar experiment tracking tools
- CI/CD pipelines for automated deployment
- Version control with Git.
- Monitoring tools for tracking model performance and drift.
- Experience with LLMs and generative AI (OpenAI APIs, Hugging Face, LangChain).
- Knowledge of data engineering concepts (ETL/ELT pipelines).
- Familiarity with REST frameworks such as FastAPI or Flask.
- Experience in Agile/Scrum environments.
- Understanding of data governance, model explainability (SHAP, LIME), and AI ethics.
Profile
- Strong analytical and problem-solving mindset.
- Ability to translate business needs into scalable AI solutions.
- Autonomous, proactive, and comfortable working in cross-functional teams.
- Strong communication skills with both technical and non-technical stakeholders.
- Fluent in English (French or Dutch is a plus depending on the environment).
Job Types: Full-time, Freelance
Pay: €200,00 - €600,00 per day
Work Location: Hybrid remote in Brussels