DevOps Engineer with AI Integration Skills (m/k/n)

Welcome to Upvanta, where our motto is “We Care More”.

Founded in 2018, we have grown into a dynamic IT services company with a team of over 180 dedicated professionals. Our journey has been marked by exceptional year-over-year growth, driven by our unwavering commitment to our clients.

• 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).
• 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.

• 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.