MLOps Engineer

Sedona Digital

. Bridge the gap between Data Science and Infrastructure teams .

Posted 11/4/2026full-timeRemote • Serbia, RomaniaJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in MLOps and DevOps practices, with a strong focus on designing and maintaining scalable pipelines using AWS services such as SageMaker, EKS, and EC2. Proficient in implementing CI/CD processes and ensuring cloud security best practices while collaborating effectively with cross-functional teams.

Highest-signal resume keywords

MLOpsAWS ServicesCI/CD PipelinesKubernetesPython

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Applicant Tracking System Keywords

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Hard Skills

MLOpsDevOpsAWS ServicesCI/CDKubernetesDockerTerraformPythonBashLinux

Soft Skills

Problem-SolvingCommunication

Tools & Technologies

SageMakerEKSS3EC2LambdaGitLab CIPrometheusGrafanaCloudWatchELK Stack

Industry Keywords

Infrastructure-as-CodeMachine Learning Model LifecycleCloud SecurityData EngineeringETL Processes

Tech Stack

Tools & technologies

AirflowAWSCloudDockerEC2ETLGrafanaKerasKubernetesLinuxMicroservicesPrometheusPythonPyTorchTensorflowTerraform

About the role

Key responsibilities & impact

Bridge the gap between Data Science and Infrastructure teams
Support the Data Science team in MLOps-related tasks and assist with DevOps initiatives
Design, implement, and maintain scalable MLOps pipelines on AWS using SageMaker, EC2, EKS, S3, Lambda and other AWS tools
Coordinate with the platform team to troubleshoot Kubernetes clusters (EKS) and orchestrate deployment of machine learning models and microservices
Develop and maintain CI/CD pipelines for model and application deployment, testing, and monitoring
Collaborate with Data Science and DevOps teams 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 in AWS infrastructure
Monitor, troubleshoot, and optimize data and ML pipelines for high availability and performance
Set up and manage model monitoring systems for performance drift and continuous model improvement
Provision cloud resources using infrastructure-as-code tools such as Terraform

Requirements

What you’ll need

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 of the 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
Advantage: experience with serverless architectures and event-driven processing on AWS
Advantage: familiarity with advanced Kubernetes concepts such as Helm
Advantage: experience with Data Engineering pipelines, ETL processes, or big data platforms
Advantage: experience with ML frameworks like TensorFlow, PyTorch and Keras
Advantage: experience with ML platforms like Kubeflow and/or SageMaker
Advantage: experience with workflow engines like Argo Workflows and/or Airflow

Benefits

Comp & perks

Work from home
Medical insurance given after the probation period
Training on market trends and client needs
Continuous learning and personal development opportunities
Holiday allowance in line with local legislation
Competitive compensation package
Performance-based bonus
Fun budget for team events