ML Ops

Your responsibilities

• Collaborate with data scientists, engineers, and analysts to understand business requirements and design machine learning solutions
• Participate in the end-to-end lifecycle of data science projects through the use of DevOps, code, experiment and model management, CI/CD and further industry best practices
• Write well-designed, testable, efficient code
• Work closely with engineering to continuously improve the way to deploy models in production
• Collaborate with cross-functional teams to integrate machine learning models into existing systems and processes, ensuring scalability and efficiency

Our requirements

• 3+ years Python - Strong experience with Python, especially in writing clean, modular, and maintainable code following OOP principles
• AWS Cloud understanding (experience with S3, EC2, Lambda)
• Docker - Experience in containerizing applications using Docker and optimizing Docker images for various environments
• Gitlab/github - Proficiency in source control management with GitLab or GitHub, including CI/CD pipelines configuration and management
• REST APIs - Knowledge and hands-on experience in developing, consuming, and managing RESTful APIs
• Machine Learning
• Experience with Argo Workflows or Argo CD for orchestrating complex workflows and managing Kubernetes-based deployments
• Proficiency in writing Bash scripts for automation tasks, system configuration, and maintenance
• Kubeflow, Airflow, Dagster - Experience with Kubeflow or similar tool for running machine learning workflows on Kubernetes