DevOps Engineer (Remote)

Your key responsibilities:

• Design and maintain CI/CD pipelines incorporating AI/ML tools and frameworks.
• Collaborate with AI teams to deploy and scale machine learning models into production environments.
• Integrate AI-based monitoring, alerting, and analytics tools to enhance pipeline visibility and enable predictive analysis.
• Optimize infrastructure resource utilization for AI applications across cloud, on-premises, or hybrid environments.
• Ensure robust security practices within AI-driven pipeline ecosystems.
• Troubleshoot pipeline and infrastructure issues, particularly those involving AI components, and guarantee high availability.
• Monitor system performance and implement improvements using AI-enhanced tools.

Skills and attributes for success:

• Expertise in DevOps tools such as Jenkins, GitHub Actions, Kubernetes, Docker, and Terraform.
• Strong understanding of cloud platforms (AWS, Azure, Google Cloud) and their AI/ML services.
• Familiarity with pipeline optimization tools like MLflow, Kubeflow, or Airflow tailored for AI workflows.
• Proficiency in scripting and programming languages (Python, Bash, YAML, etc.).
• Ability to implement automated testing frameworks for validating AI models and workflows.
• Experience in creating secure environments specific to AI-driven systems.
• Proven capability to integrate AI-powered monitoring tools like AIOps, Grafana with ML plugins, or custom AI-based diagnostics.

Must have:

• Bachelor's degree in Engineering, IT, Science, other Technical qualification
• Demonstrable experience with DevOps tools and frameworks commonly used in AI/ML workflows.
• 5+ years in a related position.

Ideally, you’ll also have

• Certifications in DevOps or AI/ML (e.g., Kubernetes, AWS Machine Learning Specialty).
• Experience working with AIOps platforms.
• Knowledge of specialized data pipeline automation tools designed for machine learning contexts.
• Hands-on experience with AI/ML frameworks like TensorFlow, PyTorch, or Scikit-learn, especially in production environments.