Data Scientist/ML Engineer

V-IT is looking for a Data Scientist/ML Engineer for a client in Brussels.
 

Jouw rol

1. Model Development and Deployment

Deploy machine learning models, spec. using Amazon SageMaker.

Monitor and maintain the performance and scalability of deployed models, in both cloud and on-premise environments.

Implement best practices for version control, model tracking, and model lifecycle management.

2. Infrastructure Management

Design and manage scalable, reliable, and secure cloud and on-premise infrastructure for machine learning projects.

Ensure seamless integration between different infrastructure components.

3. DevOps Integration

Implement and maintain CI/CD pipelines for machine learning projects.

Adopt sound Infrastructure as Code (IaC) principles to ensure consistency and repeatability, enhancing data-driven workflows.

Automate testing, deployment, and monitoring of ML models to ensure high availability and performance.

4. Collaboration and Communication

Work closely with data scientists, data engineers, and other stakeholders to understand project requirements and deliver optimal solutions.

Communicate complex technical concepts to non-technical stakeholders effectively.

5. Continuous Improvement

Stay up-to-date with the latest developments in machine learning, cloud technologies, and DevOps and MLOps practices.

Identify and implement improvements to existing workflows and systems, incl. FinOps.

6. Incident Response and Troubleshooting

Participate in incident response activities, especially those related to data integrity and service availability, to help teams dig into root cause analysis.

Help troubleshoot and resolve performance or data quality related issues promptly.

Jouw profiel

Master’s degree in Computer Science, Data Science, Engineering, or a related field.

Proven experience as a Machine Learning Engineer or similar role.

Extensive experience with Amazon SageMaker and AWS services.

Strong understanding of machine learning operations (MLOps) and model lifecycle management.

Hands-on experience with cloud and on-premise infrastructure.

Proficiency in Python and familiarity with other programming languages such as R, Java, or C++.

Experience with DevOps tools and practices, including CI/CD pipelines, containerization (Docker), and orchestration (Kubernetes, ECS).

Experience with other ML platforms and frameworks such as TensorFlow, PyTorch, and Scikit-learn.

Familiarity with big data technologies like Spark, and Kafka.

Knowledge of SQL and NoSQL databases.

Excellent problem-solving skills and the ability to think critically and creatively.

Strong communication and collaboration skills.

Ability to work independently and manage multiple tasks simultaneously.