
Senior MLOps/DevOps Engineer (EU remote)
- Warszawa, mazowieckie
- Stała
- Pełny etat
- Design, build, and maintain the infrastructure required for efficient development, deployment, and monitoring of machine learning models.
- Implement CI/CD pipelines for machine learning applications.
- Develop and manage cloud-based and on-premises solutions for model training, deployment, and monitoring.
- Ensure the scalability, reliability, and performance of machine learning systems.
- Collaborate with data scientists to understand and implement requirements for model serving, versioning, and reproducibility.
- Monitor and optimize model performance in production, identifying and resolving issues proactively.
- Automate repetitive tasks to improve efficiency and reduce the risk of human error.
- Maintain documentation and provide training to team members on MLOps best practices.
- Stay updated with the latest developments in MLOps tools, technologies, and methodologies.
- Communicate and share knowledge with other team members and actively participate in various learning-sharing opportunities
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- 3+ years of experience in MLOps, DevOps, or related fields.
- Strong programming skills in Python, with experience in other languages such as Java, C++, or Scala being a plus.
- Experience with ML frameworks such as TensorFlow, PyTorch, and/or scikit-learn.
- Proficiency with CI/CD tools such as Jenkins, or GitLab CI.
- Hands-on experience with cloud platforms such as AWS, Google Cloud, or Azure.
- Familiarity with containerization and orchestration tools like Docker and Kubernetes.
- Knowledge of infrastructure-as-code tools such as Terraform or CloudFormation.
- Strong understanding of machine learning lifecycle, including data preprocessing, model training, evaluation, and deployment.
- Excellent problem-solving skills and the ability to work independently as well as part of a team.
- Strong communication skills and the ability to explain complex technical concepts to non- technical stakeholders.
- Very good English language skills, both written and verbal (min. B2)
- Experience with feature stores, model registries, and monitoring tools such as MLflow, Tecton, or Seldon.
- Familiarity with data engineering tools like Apache Spark, Kafka, or Airflow.
- Knowledge of security best practices for machine learning systems.
- Experience with A/B testing and model performance monitoring