DevOps Engineer

20.2k - 31.1k PLN20 200 - 31 100 PLN/ mies./ mies.B2BB2B (netto)

MidZdalnie·WarszawaFull-time·B2B

#474593·Dodano dziś·0

Źródło: SOLID.Jobs

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Tech Stack / Keywords

DevOps Azure CI/CD AWS PythonAIML Terraform Kubernetes

Podsumowanie oferty

You will design and maintain CI/CD pipelines for AI/ML workflows, deploy and scale machine learning models in production, and improve pipeline monitoring and infrastructure. The role focuses on DevOps tools and cloud platforms including Kubernetes, Terraform, AWS, and Azure.

Wymagania

Expertise in 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 such as MLflow, Kubeflow, or Airflow 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 creating secure environments for AI-driven systems.
Proven capability to integrate AI-powered monitoring tools such as AIOps, Grafana with ML plugins, or custom AI-based diagnostics.
Bachelor's degree in Engineering, IT, Science, or another technical qualification.
Demonstrable experience with DevOps tools and frameworks commonly used in AI/ML workflows.
5+ years in a related position.
4 years of experience in a similar position.

Nice to have:

Certifications in DevOps or AI/ML, such as Kubernetes or AWS Machine Learning Specialty certifications.
Experience working with AIOps platforms.
Knowledge of specialized data pipeline automation tools for machine learning contexts.
Hands-on experience with TensorFlow, PyTorch, or Scikit-learn, especially in production environments.

Obowiązki

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 improve 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 ensure high availability.
Monitor system performance and implement improvements using AI-enhanced tools.

Benefity

Elastyczne godziny pracy (100%).

Elastyczne godziny

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