MLOps Engineer

This is a fully remote position, open to applicants in Serbia, +1 more country.

📋 Description

• Act as a liaison between the Data Science and Infrastructure teams.

• Assist the Data Science team with tasks related to MLOps and support DevOps initiatives.

• Create, implement, and manage scalable MLOps pipelines on AWS, utilizing tools such as SageMaker, EC2, EKS, S3, Lambda, and others.

• Collaborate with the platform team to diagnose issues within Kubernetes clusters (EKS) and facilitate the deployment of machine learning models and microservices.

• Develop and oversee CI/CD pipelines for the deployment, testing, and monitoring of models and applications.

• Work alongside Data Science and DevOps teams to enhance the model development lifecycle from experimentation to production deployment.

• Enforce security best practices, which include network security, data encryption, and role-based access controls within AWS infrastructure.

• Monitor, troubleshoot, and refine data and ML pipelines to ensure high availability and performance.

• Establish and manage model monitoring systems to track performance drift and enable continuous model enhancement.

• Provision cloud resources using infrastructure-as-code tools such as Terraform.

⛳️ Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related discipline.

• Minimum of 2 years of practical experience in MLOps, DevOps, or related fields.

• Knowledge and preferably hands-on experience with AWS services for machine learning, including SageMaker, EKS, S3, EC2, Lambda, and others.

• Familiarity with Kubernetes for container orchestration.

• Experience with Docker.

• Exposure to infrastructure-as-code tools like Terraform or CloudFormation.

• Understanding of CI/CD tools such as GitLab CI.

• Comprehension of the machine learning model lifecycle.

• Familiarity with monitoring and logging tools such as Prometheus, Grafana, CloudWatch, and ELK Stack.

• Knowledge of networking principles and cloud security best practices.

• Proficiency in Python and Bash, with a comfort level in Linux environments.

• Excellent problem-solving abilities and communication skills.

• Advantageous: experience with serverless architectures and event-driven processing within AWS.

• Advantageous: familiarity with advanced Kubernetes concepts such as Helm.

• Advantageous: experience with Data Engineering pipelines, ETL processes, or big data platforms.

• Advantageous: experience with ML frameworks like TensorFlow, PyTorch, and Keras.

• Advantageous: experience with ML platforms such as Kubeflow and/or SageMaker.

• Advantageous: experience with workflow engines like Argo Workflows and/or Airflow.

🏝️ Benefits

• Opportunity to work from home.

• Medical insurance provided after the probation period.

• Training on market trends and client requirements.

• Opportunities for continuous learning and personal development.

• Holiday allowance in accordance with local laws.

• Competitive compensation package.

• Performance-based bonus incentives.

• Fun budget allocated for team events.