As an Machine Learning Engineer, you will play a key role in developing end-to-end solutions for AI use cases. By taking up a use case right from the start you will help business define and scope the problem, pick the right ML solution and technology as well as ensure implementation, integration and deployment of your solution to production. You will do so while ensuring that our internal governance processes and best practices are respected. You will also collaborate with other team members in knowledge sharing as well as continuously improving our way of working.
Responsibilities
1. Develop AI applications using state-of-the-art (Gen)AI technology;
2. Translate business requests into AI applications;
3. Identify high-value use cases through data exploration and visualization;
4. Pilot prototypes in production processes to demonstrate their value;
5. Deploy prototypes to production, with support of IT, to obtain reliable, scalable systems;
6. Present your results in a clear manner and discuss them with multi-functional project teams;
7. Work in close collaboration with business experts ( for requirement gathering, data source identification, data and process understanding, feature engineering, result validation, etc.), with IT ( for ETL, deployment to production, etc.) and with other AI engineers in the team ( for knowledge sharing).
Profile
8. PhD or Master's degree in a quantitative field (Artificial Intelligence, Computer Science, Engineering, Mathematics, etc.)
9. 3+ years of relevant work experience in a business environment
Technical skills
10. Hands-on experience with Python and its AI ecosystem;
11. Strong knowledge of state-of-the-art AI and statistical methods;
12. Experience with cloud technology (Azure);
13. Experience using LLM’s and commonly used libraries to interact with LLM’s (Langchain, Llamaindex, ...);
14. Proven proficiency in the end-to-end AI project life cycle, including:
- Translating business requests into data requirements.
- Identifying high-value use cases through data exploration and visualization.
- Developing AI solutions (incl. feature engineering, model fitting, etc.).
- Deploying scalable AI applications to production.
15. Any experience with following elements is a big plus:
- MLOps practices, orchestration with Airflow, Gitlab CI/CD, etc.
- Containerized deployment of ML products – docker, podman, Kubernetes
Attitudes/Behavior
16. Passionate about AI and a constant learner;
17. Result-oriented and highly proficient in transforming data into actionable insights that create business value;
18. Team player with strong communication and presentation skills;
19. Able to manage AI projects in an autonomous way and drive collaboration with domain experts, data engineers and other AI engineers;
20. Knowledge in the field of telecommunications is a plus.
Languages
21. Fluent in Dutch and/or French (mandatory) + Fluent in English.