Summary
✨ AI‑Generated
A technology organization is seeking an MLOps Engineer to build and maintain scalable machine-learning infrastructure in a hybrid environment. The position covers cloud-based ML pipelines, Kubernetes orchestration, CI/CD, security, monitoring, and production optimization.
Highlights
Competitive gross annual salary, a hybrid working model, and the opportunity to build scalable machine-learning infrastructure while collaborating with data science and DevOps teams.
Description
Locations: Athens (Michalakopoulou
Salary: 65,000 - 70,000 Euros gross annually
Model: Hybrid (3 office - 2 home)
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 toolsCoordinate with our platform team to troubleshoot Kubernetes clusters (EKS) to orchestrate the deployment of machine learning models and other microservicesDevelop and maintain CI/CD pipelines for model and application deployment, testing, and monitoringCollaborate closely with Data Science, and DevOps team to streamline the model development lifecycle, from experimentation to production deploymentImplement security best practices, including network security, data encryption, and role-based access controls within the AWS infrastructureMonitor, troubleshoot, and optimize data and ML pipelines to ensure high availability and performanceSet up and manage model monitoring systems for performance drift, ensuring continuous model improvementMain requirements:
Bachelor’s degree in Computer Science, Engineering, or related field2+ years of hands-on experience in MLOps, DevOps, or related fieldsKnowledge and preferable working experience in AWS services for machine learning, such as SageMaker, EKS, S3, EC2, Lambda, and othersExposure to Kubernetes for container orchestrationExperience with DockerExposure to infrastructure-as-code tools such as Terraform or CloudFormationFamiliarity with CI/CD tools such as GitLab CIUnderstanding machine learning model lifecycleFamiliarity with monitoring and logging solutions like Prometheus, Grafana, CloudWatch and ELK StackUnderstanding of networking concepts and cloud security best practicesProficiency in Python and Bash, and comfortable working in Linux environmentsStrong problem-solving and communication skillsThe following will be considered an advantage:
Experience working with serverless architectures and event-driven processing on AWSFamiliarity with advanced Kubernetes concepts such as HelmExperience with Data Engineering pipelines, ETL processes, or big data platformsExperience with ML frameworks like TensorFlow, PyTorch and KerasExperience with ML platforms like Kubeflow and/or SageMakerExperience with workflow engines like Argo Workflows and/or AirflowBenefit from:
Attractive remuneration package plus performance related rewardIntellectually stimulating work environmentContinuous personal development and international training opportunities
