DevOps Engineer with AI Integration Skills

As a DevOps Engineer with AI integration skills, you will collaborate across multiple implementation streams and work closely with agile teams to achieve project and client goals. You will participate in international projects based on the latest data technologies and cloud platforms.

• Bachelor’s degree in Engineering, IT, Science, or a related technical field.
• At least 5 years of experience in a corporate IT environment in a similar role.
• Proven experience with DevOps tools and frameworks used in AI/ML workflows.
• Strong communication skills and ability to collaborate with global teams.

Nice to 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 for machine learning.
• Hands-on experience with AI/ML frameworks such as TensorFlow, PyTorch, or Scikit-learn in production environments.

Skills and attributes for success:

• Hands-on experience with 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 AI workflow tools such as MLflow, Kubeflow, or Airflow.
• Proficiency in scripting and programming languages (Python, Bash, YAML, etc.).
• Ability to implement automated testing frameworks for validating AI models and workflows.
• Experience building secure environments for AI-driven systems.
• Experience integrating AI-powered monitoring tools (AIOps, Grafana with ML plugins, or custom AI diagnostics).

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