Seargin

Careers At Seargin

Break into the IT industry without coding or tech skills and join teams working on international projects.

MLOps Engineer

Seargin is a dynamic multinational tech company operating in 50 countries. At Seargin, we drive innovation and create projects that shape the future and greatly enhance the quality of life. You will find our solutions in the space industry, supporting scientists in the development of cancer drugs, and implementing innovative technological solutions for industrial clients worldwide. These are just some of the areas in which we operate.

Position:

MLOps Engineer

Location:

Remote

Country:

European Union

Form of employment:

B2B

Experience level:

Mid

Responsibilities:

  • Kubernetes-based Platform Development

    Lead the development and maintenance of the Kubernetes-based Platform on AWS, ensuring optimal performance and scalability

  • Kubeflow Integration and Management

    Design, execute, and manage the deployment of Kubeflow on AWS, leveraging its capabilities for machine learning workflows efficiently

  • Terraform Deployment Automation

    Automate the deployment process using Terraform, ensuring consistent and reliable infrastructure provisioning for the Platform

  • Cloud Infrastructure Design and Implementation

    Architect and implement cloud infrastructure on AWS using Terraform and AWS services, optimizing resource allocation and performance for the Platform

  • GitOps and DevOps Implementation

    Implement GitOps and DevOps best practices throughout the deployment process, ensuring efficient and automated infrastructure management and deployment pipelines

  • Collaborative Software Development Support

    Engage in daily collaboration with software development teams, facilitating effective communication channels and fostering a culture of teamwork to ensure seamless integration of DevOps practices and agile methodologies

  • Security and Compliance

    Implement security best practices and compliance standards for machine learning systems, ensuring data privacy, integrity, and regulatory compliance

What we offer

Requirements:

  • AWS EKS Deployment and Management

    Demonstrate a deep understanding of deploying and managing Kubernetes applications on Amazon Elastic Kubernetes Service (AWS EKS), ensuring efficient and reliable operation of containerized workloads

  • Kubernetes Application Design Proficiency

    Possess expertise in Kubernetes application design using tools like Kustomize and Helm, enabling effective management and configuration of complex application deployments

  • Infrastructure-as-Code (IaC) Proficiency

    Showcase experience with infrastructure-as-code (IaC) tools such as Terraform, AWS CDK, and CloudFormation, facilitating automated provisioning and management of cloud resources

  • Cloud Platform Experience

    Demonstrate experience with cloud platforms, particularly AWS, showcasing familiarity with its services and capabilities for building scalable and resilient infrastructure

  • Expertise in DevOps Practices

    Demonstrate expertise in DevOps practices, Agile practices, ensuring efficient collaboration and software delivery processes and infrastructure management

  • Proficiency in Git, Linux, and Bash Scripting

    Over 2 years of experience in utilizing Git for version control, Linux fundamentals, and Bash scripting for automation tasks

  • Python Programming Expertise

    Demonstrated proficiency in Python programming language with over 2 years of hands-on experience

  • Containerization Concepts Mastery

    Over 2 years of experience with container concepts, including Docker and Kubernetes, for efficient deployment and management of applications

  • CI/CD Pipeline Design and Implementation

    Designing and implementing Continuous Integration / Continuous Delivery pipelines for over 2 years, utilizing tools like GitLab and Argo CD

  • Superior Troubleshooting Approach

    Demonstrated ability to troubleshoot issues effectively, ensuring timely resolution and minimal disruption to operations

Nice to have

  • Kubeflow Management in Cloud Environment

    Experience managing and deploying Kubeflow in a cloud environment is beneficial, enhancing capabilities in leveraging machine learning workflows effectively

  • Availability for Travel

    It is advantageous to be available to travel, as occasional workshops in locations such as Warsaw and Poznan may be conducted, fostering collaboration and knowledge exchange

  • MLOps Tools Experience

    Experience with any MLOps tools such as MLflow, , or Amazon SageMaker is beneficial, enhancing proficiency in managing machine learning workflows efficiently

  • Data Science Basics Understanding

    A good understanding of Data Science basics, including concepts like train/test model data sets, overfitting, classification, and clustering, is advantageous, facilitating collaboration with data scientists and effective communication on machine learning projects

Apply & join the team




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