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MLOps Engineer

  • Athens

  • €50000 - €70000 per annum

The main responsibilities of the position include:

  • Assist in designing, implementing, and maintaining scalable MLOps pipelines on AWS using services such as SageMaker, EC2, EKS, S3, Lambda and other relevant AWS tools
  • Coordinate with our platform team to troubleshoot Kubernetes clusters (EKS) to orchestrate the deployment of machine learning models and other microservices
  • Develop and maintain CI/CD pipelines for model and application deployment, testing, and monitoring
  • Collaborate closely with Data Science, and DevOps team to streamline the model development lifecycle, from experimentation to production deployment
  • Implement security best practices, including network security, data encryption, and role-based access controls within the AWS infrastructure
  • Monitor, troubleshoot, and optimize data and ML pipelines to ensure high availability and performance
  • Set up and manage model monitoring systems for performance drift, ensuring continuous model improvement

Main requirements:

  • Bachelor's degree in Computer Science, Engineering, or related field
  • 1+ years of hands-on experience in MLOps, DevOps, or related fields
  • Knowledge and preferable working experience in AWS services for machine learning, such as SageMaker, EKS, S3, EC2, Lambda, and others
  • Exposure to Kubernetes for container orchestration
  • Experience with Docker
  • Exposure to infrastructure-as-code tools such as Terraform or CloudFormation
  • Familiarity with CI/CD tools such as GitLab CI
  • Understanding machine learning model lifecycle
  • Familiarity with monitoring and logging solutions like Prometheus, Grafana, CloudWatch and ELK Stack
  • Understanding of networking concepts and cloud security best practices
  • Proficiency in Python and Bash, and comfortable working in Linux environments
  • Strong problem-solving and communication skills

The following will be considered an advantage:

  • Experience working with serverless architectures and event-driven processing on AWS
  • Familiarity with advanced Kubernetes concepts such as Helm
  • Experience with Data Engineering pipelines, ETL processes, or big data platforms
  • Experience with ML frameworks like TensorFlow, PyTorch and Keras
  • Experience with ML platforms like Kubeflow and/or SageMaker
  • Experience with workflow engines like Argo Workflows and/or Airflow

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