MLOps Engineer

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