MLOps Engineer

Required skills

• 10+ years of professional experience in building applications using cloud services. Prior experience in building Machine Learning platforms using cloud services.
  
• Cloud expertise: Deep knowledge of cloud platforms like AWS, Google Cloud Platform, or Azure, including their machine learning and data services (Azure preferred).
  
• DevOps skills: Experience with CI/CD pipelines, infrastructure as code, and containerization technologies like Docker and Kubernetes.
  
• Machine learning knowledge: Understanding of ML workflows, model training, and deployment processes.
  
• Data engineering: Familiarity with data pipelines, ETL processes, and data storage solutions.
  
• Software engineering: Strong programming skills, particularly in languages commonly used in ML like Python.
  
• System design: Ability to architect scalable, reliable systems that integrate various services.
  
• Automation: Expertise in automating workflows and processes across the ML lifecycle.
  
• Security and compliance: Knowledge of best practices for securing ML pipelines and ensuring regulatory compliance.
  
• Monitoring and logging: Experience setting up monitoring and logging for ML systems.
  
• Collaboration: Ability to work with data scientists, software engineers, and other stakeholders.