MLOps Engineer

XM; Poland; On-site; PLN 180,000 - 240,000; Full time

Benefits offered by this job

Private health insurance

Job summary

XM is seeking an MLOps Engineer to bridge Data Science and Infrastructure, building scalable ML pipelines on AWS. You will implement CI/CD for models, manage deployments with Kubernetes, and enforce security practices across cloud resources.

Collaboration with data scientists and DevOps will drive production-ready ML systems. Ideal candidates have 2+ years in MLOps/DevOps, strong Python and Bash skills, and hands-on experience with Terraform, Docker, and cloud monitoring tools.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field.
• 2+ years of hands-on experience in MLOps, DevOps, or related fields.
• Knowledge of AWS services for ML (SageMaker, EKS, S3, EC2, Lambda) and other AWS tools.
• Exposure to Kubernetes for container orchestration.
• Experience with Docker.
• IaC tools such as Terraform or CloudFormation.
• Familiarity with CI/CD tools like GitLab CI.
• Understanding of ML lifecycle, monitoring, and logging with Prometheus, Grafana, CloudWatch, ELK.
• Proficiency in Python and Bash in Linux environments.
• Strong problem-solving and communication skills.

Responsibilities

• Assist in designing, implementing, and maintaining scalable MLOps pipelines on AWS.
• Coordinate with platform team to troubleshoot Kubernetes clusters (EKS) for ML deployments.
• Develop and maintain CI/CD pipelines for model and app deployment, testing, and monitoring.
• Collaborate with Data Science and DevOps teams to streamline the ML lifecycle from experiment to production.
• Implement security best practices, including network security and data encryption in AWS.
• Monitor and optimize data and ML pipelines for high availability and performance.
• Set up and manage model monitoring systems for performance drift and improvement.

Skills

MLOps

DevOps

Python

Bash

Linux

Communication

Problem solving

Education

Bachelor’s degree in CS/Engineering

Tools

AWS

SageMaker

EKS

S3

EC2

Lambda

Docker

Terraform

GitLab CI

Kubernetes

Prometheus

Grafana

CloudWatch

ELK Stack

Helm

Argo Workflows

Airflow

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

All applications will be treated with strict confidentiality!