MLOps Engineer

XM; Polska; Remote; PLN 355,000 - 474,000; Full time

Benefits offered by this job

Private health insurance

Intellectually stimulating work

Continual international training

Personal development opportunities

Job summary

XM is seeking a skilled MLOps Engineer to join our team and bridge Data Science and Infrastructure. You will support the Data Science team with MLOps tasks while driving DevOps initiatives, including CI/CD pipelines, cloud provisioning with Terraform, and Kubernetes orchestration.

Work with data scientists and engineers to deploy data pipelines, train models, and manage deployments in scalable cloud environments, ensuring security, performance, and reliability across the ML lifecycle.

Qualifications

• Bachelor's degree in CS, engineering, or related field.
• 2+ years in MLOps, DevOps, or related area.
• Experience with AWS ML services (SageMaker, EKS, S3, EC2, Lambda).
• Kubernetes expertise and Docker experience.
• Familiarity with Terraform or CloudFormation.
• CI/CD tooling experience (GitLab CI).
• Understanding ML lifecycle and deployment.
• Networking, security, and monitoring basics (CloudWatch, Prometheus).

Responsibilities

• Design and maintain scalable MLOps pipelines on AWS (SageMaker, EKS).
• Coordinate with platform team to deploy ML models and microservices on Kubernetes.
• Develop and maintain CI/CD pipelines for model deployment and testing.
• Collaborate with Data Science and DevOps to streamline ML lifecycle from experiment to prod.
• Implement security best practices for AWS networks and data protection.
• Monitor data and ML pipelines for performance and reliability.
• Set up model monitoring for drift and continuous improvement.

Skills

MLOps

DevOps

Python

Bash

Kubernetes

Docker

Terraform

CI/CD

AWS SageMaker

Cloud security

Networking basics

Communication

Problem solving

Education

Bachelor’s degree

Tools

GitLab CI

Terraform

CloudFormation

Prometheus

Grafana

ELK Stack

Job description

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
• 2+ 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:

• Private health insurance
• 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

Location

Cyprus, Greece or Fully Remote - in other eligible locations

All applications will be treated with strict confidentiality!