MLOps Engineer

As an MLOps Engineer, you are the driving force behind the operationalization of our machine learning models. You will design, implement, and maintain robust pipelines for training, deployment, and monitoring of our AI solutions to ensure their scalability and reliability.

Tasks and Responsibilities

• ● Development and maintenance of CI/CD pipelines for machine learning models
• ● Automation of model training, validation, and deployment
• ● Monitoring the performance and reliability of production ML systems
• ● Collaboration with Data Scientists and Backend Developers for model integration
• ● Management of infrastructure for ML workloads (e.g., Kubernetes, Kubeflow)
• ● Ensuring data quality and versioning in ML pipelines
• ● Implementation of best practices for reproducible and scalable machine learning

Requirements

• ● At least 2 years of experience in MLOps or DevOps with a focus on ML
• ● Excellent knowledge of Python and common ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
• ● Experience with containerization (Docker) and orchestration (Kubernetes)
• ● Practical experience with cloud platforms (AWS, GCP, or Azure) and their ML services
• ● Knowledge of CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions)
• ● Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack)
• ● Solid understanding of software engineering principles

Preferred Qualifications

• ○ Experience with workflow management tools like Apache Airflow or Kubeflow Pipelines
• ○ Knowledge of Infrastructure as Code (Terraform, CloudFormation)
• ○ Understanding of data engineering concepts
• ○ Experience with feature stores
• ○ Contributions to open-source MLOps projects

What We Offer

• ● Flexible working hours and remote option
• ● Regular team events and knowledge sharing
• ● State-of-the-art work equipment of your choice

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