MLOps Engineer

About the role

We are seeking a skilled MLOps Engineer to bridge our Data Science and Infrastructure teams. You will design, implement, and maintain scalable machine‑learning pipelines on AWS, ensuring reliable, secure, and high‑performance deployments.

Key responsibilities

• Design and operate MLOps pipelines on AWS services such as SageMaker, EC2, EKS, S3, and Lambda.
• Collaborate with the platform team to troubleshoot and manage Kubernetes (EKS) clusters for model and micro‑service deployment.
• Develop and maintain CI/CD pipelines for model and application delivery, testing, and monitoring.
• Implement security best practices, including network security, data encryption, and role‑based access controls.
• Monitor, troubleshoot, and optimise data and ML pipelines for high availability and performance.
• Set up model monitoring systems to detect performance drift and drive continuous improvement.

Required profile

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• 2+ years of hands‑on experience in MLOps, DevOps, or related disciplines.
• Strong understanding of the machine‑learning model lifecycle.
• Excellent problem‑solving and communication skills.

Required skills

• AWS services: SageMaker, EC2, EKS, S3, Lambda.
• Kubernetes and Docker container orchestration.
• Infrastructure‑as‑code tools: Terraform or CloudFormation.
• CI/CD platforms: GitLab CI.
• Programming: Python and Bash in Linux environments.
• Monitoring and logging: Prometheus, Grafana, CloudWatch, ELK Stack.
• Networking concepts and cloud security best practices.

What we offer

• Attractive remuneration package plus performance‑related reward.