MLOps Engineer

As an MLOps Engineer, you will be responsible for managing and optimizing the machine learning lifecycle, from model development to deployment and monitoring. You will work closely with data scientists, software engineers and IT operations teams to ensure seamless integration, scalability and reliability of machine learning models in production environments. The ideal candidate will have a strong background in both machine learning and DevOps, with experience in building and maintaining robust MLOps pipelines.

• Pipeline Development: Design, implement and maintain scalable and efficient machine learning pipelines that automate the process of model training, testing, deployment and monitoring.
• Model Deployment: Collaborate with data scientists to deploy machine learning models to production environments, ensuring they are scalable, reliable and secure.
• CI/CD Integration: Develop and maintain continuous integration and continuous deployment (CI/CD) processes for machine learning models, ensuring seamless updates and version control.
• Infrastructure Management: Set up and manage cloud-based and on-premise infrastructure for machine learning workflows, including data storage, computing resources and model serving platforms.
• Monitoring and Maintenance: Monitor the performance and health of deployed models, implementing automated systems for anomaly detection, logging and alerting to ensure high availability and performance.
• Collaboration: Work closely with cross-functional teams, including data scientists, software developers and IT operations, to define requirements and deliver solutions that meet business and technical needs.
• Security: Implement best practices for data security, model governance and compliance, ensuring that machine learning workflows adhere to industry standards and regulations.
• Documentation: Maintain comprehensive documentation of MLOps processes, infrastructure and best practices for future reference and reproducibility.
• Innovation: Stay current with the latest advancements in MLOps tools and technologies, continuously improving and evolving the MLOps processes and infrastructure.

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field
• Experience: 3+ years of experience in machine learning, DevOps, or a related field, with specific experience in MLOps
• Technical Skills:
  • Proficiency in programming languages such as Python, Go, Rust, or similar
  • Experience with ML/DL frameworks such as TensorFlow, TensorRT, PyTorch, or scikit-learn
  • Strong knowledge of DevOps practices, including CI/CD, infrastructure as code (IaC) and containerization (Docker, Kubernetes)
  • Experience with version control systems (e.g., Git)
  • Understanding of data engineering concepts and tools for data preprocessing and ETL
  • Knowledge of monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack)
  • Experience with tools such as ClearML for ML lifecycle management
  • Experience getting ML products to production
  • Experience with cloud platforms such as AWS, Azure, or Google Cloud, with focus on Google Cloud
• Analytical Skills: Excellent analytical and problem-solving skills with the ability to design innovative solutions to complex problems
• Communication: Strong verbal and written communication skills, with the ability to effectively collaborate with technical and non-technical stakeholders
• Attention to Detail: High attention to detail and a commitment to ensuring the accuracy and quality of work
• Adaptability: Ability to thrive in a fast-paced, dynamic environment and manage multiple projects simultaneously

• An excellent work environment and an opportunity to create a real impact in the world
• A truly high-tech, state-of-the-art engineering company with flat structure and no politics
• Working with the very latest technologies in Data & AI, including Edge AI, Swarming - both within our software platforms and within our embedded on-board systems
• Flexible work arrangements
• Professional development opportunities
• Collaborative and inclusive work environment
• Salary compatible with the level of proven experience