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Wallet icon Coin icon Rate 7 600 € - 8 000 € / month info
Timer icon Form of cooperation Full-time
Briefcase icon Sector Logistics
Location icon Location 100% Remote

info The reward is calculated upon delivery of 20 MD per month (1MD=8h)

Project duration 6 months with the possibility of extension
Period of cooperation 13.04.2026 - 12.10.2026
Start date 13.04.2026 - 27.04.2026
Technology
  • AWS Amazon Web Services
  • MS Azure
  • Kubernetes
  • Python
  • Docker
  • Terraform
  • LLM Large Language Model
  • Grafana
  • Github
  • Prometheus
  • Kubeflow
Languages
  • English flag English - active, B2/C1/C2

Project description

  • design, development, and maintenance of reliable cloud and AI/ML platforms for massive enterprise utilization
  • collaboration within a dedicated DevOps team on the development of end-to-end ML workflows and solving complex business challenges through the integration of GenAI and LLM models into a production environment
  • architecture and management of infrastructure as code with intensive use of AWS, Azure, Kubernetes, Terraform technologies, and advanced development in Python
  • building and optimizing CI/CD pipelines through GitHub Actions and ensuring comprehensive secure deployment
  • setting up and maintaining advanced monitoring and logging using the Prometheus, Grafana, and Loki stack (without the need for on-call support)
  • containerizing applications via Docker and managing the machine learning lifecycle using tools like Kubeflow and SageMaker
  • collaboration takes place in a 100% remote mode within the entire EU

Project requirements

  • advanced experience with:
    • software architecture development and design in Python (minimum 5 years of experience)
    • cloud platform, primarily AWS or Azure (including services like SageMaker or Bedrock)
    • container orchestration and cluster management in Kubernetes (e.g., EKS)
    • infrastructure as code and its automation using Terraform
    • MLOps tools, especially integration and maintenance of Kubeflow
  • experience with:
    • containerization and optimization of performance and security using Docker
    • creation and debugging of complex CI/CD pipelines, ideally via GitHub Actions
    • deployment and work with end-to-end ML workflows and integration of LLM and GenAI
    • monitoring and logging systems like Prometheus, Grafana, or Loki
  • advanced knowledge of:
    • CI/CD principles, security practices, and DevOps culture
    • theoretical foundations and production deployment of a wide range of ML algorithms
    • model registry, model performance monitoring, and data quality monitoring
  • advantageous:
    • experience with advanced Kubernetes features, such as operators
    • experience with the Dynatrace platform
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