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BrusselsHybrid40 hours/week<3months
This is a long-term assignment at a technology company.
What you do
- Implement machine learning models, particularly using cloud solutions.
- Monitor and maintain the performance and scalability of implemented models, both in cloud and on-premise environments.
- Implement best practices for version control, model tracking, and model lifecycle management.
- Design and manage scalable, reliable, and secure cloud and on-premise infrastructure for machine learning projects.
- Ensure seamless integration between different infrastructure components.
- Implement and maintain CI/CD pipelines for machine learning projects.
- Apply sound Infrastructure as Code (IaC) principles to ensure consistency and repeatability, enhancing data-driven workflows.
- Collaborate closely with data scientists, data engineers, and other stakeholders to understand project requirements and deliver optimal solutions.
- Stay updated on the latest developments in machine learning, cloud technologies, and DevOps and MLOps practices.
- Identify and implement improvements in existing workflows and systems, including FinOps.
- Participate in incident handling activities, particularly those related to data integrity and service availability, to assist teams in performing root cause analysis.
- Help quickly identify and resolve issues related to performance or data quality.
Profile
- A bachelor's or master's degree in computer science, information technology, data science, or a related field.
- Proven experience as a data scientist, machine learning engineer, data engineer, or in a similar role.
- Experience performing both business and technical analyses.
- A thorough understanding of system architecture and the ability to translate business requirements into scalable technical designs and solutions.
- Experience performing data analyses with Python via scripting and Jupyter Notebooks.
- Knowledge of modern data engineering practices and frameworks, such as dbt and/or Dagster.
- Experience with Amazon SageMaker and other cloud services is a plus.
- Thorough understanding of MLOps principles, including model deployment, monitoring, and lifecycle management.
- Hands-on experience with both cloud-based and on-premises infrastructure.
- Proficiency in Python and its associated data science ecosystem.
- Familiarity with additional programming languages such as R, Java, or C++ is considered a plus.
- Experience with DevOps practices and tools, including CI/CD pipelines, containerization (Docker), and orchestration platforms such as Kubernetes or Amazon ECS.
- Thorough knowledge of SQL and familiarity with NoSQL databases.
- Excellent analytical and problem-solving skills, with the ability to think critically and creatively.
- Strong communication and stakeholder management skills, with the ability to collaborate effectively with both technical and business teams.
- The ability to work independently, effectively prioritize, and manage multiple tasks in a dynamic environment.
Practical
- Opportunities for growth and learning.
- Professional training and evolution policy.
- Flexibility and attention to work-life balance.
- Annual events and festivals.
- For freelancers: possibility to enjoy training and events.
How to apply
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