MLOps Engineer

2 weeks ago

Athens, Attica, Greece XM Full time €25,000 - €45,000 per year

MLOps Engineer

The Role:

We are looking for a skilled MLOps Engineer to join our team and play a key role in bridging the gap between our Data Science and Infrastructure teams. You will be responsible for supporting the Data Science team in MLOps-related tasks while also helping in DevOps initiatives, including CI/CD pipeline creation, provisioning of cloud resources using tools like Terraform, and Kubernetes orchestration. You will collaborate closely with data scientists and engineers to deploy data pipelines, train machine learning models, and manage their deployment within scalable cloud environments while ensuring high performance, security, and reliability throughout the ML lifecycle.

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

Benefit from:

  • Attractive remuneration package plus performance related reward
  • Private health insurance
  • Corporate pension fund
  • Intellectually stimulating work environment
  • Continuous personal development and international training opportunities

The Hiring Experience: What Awaits You

  • Let's Connect – Intro Chat with Talent Acquisition
  • Deep Dive – First Interview with Your Future Team
  • Final Connection – Final Interview

All applications will be treated with strict confidentiality

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