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MLOps Engineer

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MidFull-time
#402951·Dodano wczoraj·0
Źródło: nofluffjobs.com
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Tech Stack / Keywords

KubernetesTerraformMLOpsKubeflowAirflowAWS

Wymagania

  • Bachelor’s degree in Computer Science, Engineering, or related field
  • 2+ years of hands-on experience in MLOps, DevOps, or related fields
  • Knowledge and preferable experience with AWS services for machine learning incl. SageMaker, EKS, S3, EC2, Lambda
  • Exposure to Kubernetes for container orchestration
  • Experience with Docker
  • Experience with infrastructure-as-code tools like Terraform or CloudFormation
  • Familiarity with CI/CD tools such as GitLab CI
  • Understanding of 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; comfortable with Linux environments

Nice to have:

  • Experience with serverless architectures and event-driven processing on AWS
  • Familiarity with advanced Kubernetes concepts such as Helm
  • Experience with Data Engineering pipelines, ETL, 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

Obowiązki

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

Benefity

  • Attractive remuneration package plus performance-related reward
  • Intellectually stimulating work environment
  • Continuous personal development and international training opportunities

Inne informacje

All applications will be treated with strict confidentiality!

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