MLOps Engineer (AI/MLOps)

TL;DR

MLOps Engineer (AI/MLOps): Bridging Data Science and Infrastructure teams by supporting MLOps tasks and DevOps initiatives, including CI/CD pipeline creation, cloud resource provisioning, and Kubernetes orchestration. Focus on deploying data pipelines, training and managing machine learning models within scalable cloud environments, ensuring high performance, security, and reliability throughout the ML lifecycle.

Location: Hybrid in Limassol, Greece

Company

hirify.global is seeking an MLOps Engineer to join their Engineering team, focusing on cloud DevOps and machine learning operations.

What you will do

• Assist in designing, implementing, and maintaining scalable MLOps pipelines on AWS using services such as SageMaker, EC2, EKS, S3, and Lambda.
• Coordinate with the 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 teams to streamline the model development lifecycle.
• 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.

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, and Lambda.
• Exposure to Kubernetes for container orchestration and experience with Docker.
• Exposure to infrastructure-as-code tools such as Terraform or CloudFormation.
• Proficiency in Python and Bash, and comfortable working in Linux environments.

Nice to have

• 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.

Culture & Benefits

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

Hiring process

• Intro Chat with Talent Acquisition.
• First Interview with Your Future Team.
• Final Interview.