MLOps Engineer
Multiple
Engineering – Cloud DevOps /
Full-time /
Hybrid
MLOps Engineer – Cyprus or Greece
The Role:
We are looking for a skilled MLOps Engineer with experience in managing and scaling machine learning pipelines and infrastructure using cloud infrastructure platform (AWS) and Kubernetes. You will be responsible for building, maintaining, and optimizing the cloud-based infrastructure that supports our AI/ML models, ensuring seamless integration and deployment.
The main responsibilities of the position include:
- Design, implement, and maintain 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 set up and manage Kubernetes clusters (EKS) to orchestrate the deployment of machine learning models and other microservices
- Automate the CI/CD pipelines for model 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 machine learning pipelines to ensure high availability and performance
- Provide guidance on infrastructure costs, monitoring, and efficient scaling strategies
- 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
- 3+ 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
- Experience with Kubernetes for container orchestration, specifically experience with AWS EKS (Elastic Kubernetes Service)
- Experience with Docker for containerization and deployment of ML models
- Solid experience with infrastructure-as-code tools such as Terraform or CloudFormation
- Familiarity with CI/CD tools such as Jenkins, GitLab CI
- Strong understanding of machine learning model lifecycle and experience with tools like Kubeflow or Airflow for orchestration and workflow management
- Experience with monitoring and logging solutions like Prometheus, Grafana, CloudWatch, and ELK Stack
- Solid understanding of networking concepts and cloud security best practices
- 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, Kustomize, and Istio
- Hands-on experience with Data Engineering pipelines, ETL processes, or big data platforms
- Experience with Python and frameworks like TensorFlow or PyTorch for model deployment
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
All applications will be treated with strict confidentiality!
