Who you are

• 3-5 years of experience in MLOps, DevOps or a related field
• Bachelor’s degree in Computer Science, Data Science or a related field
• Strong understanding of machine learning principles and model lifecycle management
• Proficiency in programming languages such as Python, with hands-on experience in machine learning frameworks like TensorFlow or PyTorch
• Experience with cloud platforms like AWS, Azure, or Google Cloud and their respective machine learning services
• Familiarity with containerization and orchestration tools such as Kubernetes and Docker
• Knowledge of CI/CD pipelines, automation tools and version control systems like Git
• Strong problem-solving skills and ability to troubleshoot complex issues
• Experience with monitoring tools and practices for model performance in production
• Ability to work collaboratively in cross-functional teams
• Experience with infrastructure-as-code (IaC) tools such as Terraform or Crossplane
• Knowledge of big data technologies like Apache Spark or Hadoop
• Familiarity with data engineering practices and tools
• Experience with A/B testing and model validation in production environments
• Relevant MLOps certifications (e.g., AWS Certified Machine Learning – Specialty, DataRobot MLOps Certification) are a plus

What the job involves

• Design, develop, and implement MLOps pipelines for the continuous deployment and integration of machine learning models
• Collaborate with data scientists and engineers to understand model requirements and optimize pipeline processes
• Automate the training, testing and deployment processes for machine learning models
• Continuously monitor and maintain model pipelines, ensuring optimal performance, accuracy and reliability
• Optimize machine learning pipelines for scalability, efficiency and cost-effectiveness
• Ensure compliance with security and data privacy standards in all MLOps activities