Job Informationen

Location: Basel Workload: Full-time As Principal DevOps Engineer, you will collaborate with important stakeholders on the development of the build, release, and deploy toolchain for DevOps, paving the way for seamless and efficient software delivery processes. Key responsibilities: Lead the initiative to set up, manage, and meticulously maintain parity across development, staging, and production application environments in cutting-edge cloud infrastructure, ensuring a robust and consistent deployment pipeline. Champion the implementation of advanced monitoring infrastructure development, empowering the team with real-time insights and ensuring the highest levels of system reliability and performance. Provide dedicated on-call support for production operations, ensuring the uninterrupted delivery of critical services and swift resolution of any operational issues. Interface with software developers, product managers, test engineers and administrators on projects to design and develop the build, release, and deploy toolchain for DevOps while providing on-call support. Identify, troubleshoot and resolve issues quickly and effectively, sometimes under pressure. Actively involved in planning, high availability engineering, performance tuning, and automation/tools development. Manage multiple releases with focus on system reliability, scalability, and efficiency. Implement and manage the full lifecycle of machine learning models, including versioning, deployment strategies (e.g., canary, A/B testing), monitoring for drift and performance, and decommissioning. Bring in leadership quality to improve technology and process of devops as well as provide mentorship to other devops engineers in the team. Who You Are Bachelor's degree in Computer Science, Engineering, or a related field with a minimum of 8+ years of experience in a DevOps or equivalent combination of education and experience to perform at this level. 8+ years of experience with container technology, including Kubernetes, AWS EKS, Helm Charts, Splunk, and Docker, along with provisioning infrastructure through IAC using Terraform and cloud automation principles. Proficiency in Unix/Linux administration in Shell scripting and internals with a preference for Ubuntu. Deep working experience and extensive knowledge in building and deploying infrastructure using IaC frameworks such as terraform and AWS Cloudformation/SAM. Experience building and automating scalable data pipelines for ingesting, transforming, distributed computing and versioning large-scale image datasets. Familiarity with DevOps practices and proficiency in log analysis and monitoring tools are essential for effective troubleshooting and system optimization. Proficiency in Python for automating production systems, including Git, Gitlab, Git actions, GitHub CI/CD, familiarity with common ML libraries such as TensorFlow, PyTorch, and scikit-learn to understand the engineering needs of the ML models you will be deploying. Strong working knowledge of AWS Cloud infrastructure, including EC2, S3, API Gateway, Kubernetics, RDS, VPC peering, Route53, S3, IAM, Batch, Lambda, AWS Config and Autoscaling. Preferred: MLOps experience with demonstrated experience supporting machine learning or computer vision teams. Deep experience with container orchestration for ML workloads using Kubernetes, including frameworks like Kubeflow or KubeRay to manage distributed training jobs. Familiarity with data versioning tools like DVC. Familiarity with common ML libraries such as TensorFlow, PyTorch, and scikit-learn to understand the engineering needs of the ML models. Familiarity with other languages such as Java, R, and C/C++. Experience with AWS services for machine learning, such as Amazon SageMaker, and experience managing GPU-accelerated compute instances (e.g., EC2 P and G series) for model training and inference.

Benötigte Skills

• Linux
• Senior
• Support
• Testing
• CLOUD
• Monitoring
• Python
• Swift
• Machine Learning
• AWS
• C
• C++
• JAVA
• R
• Shell
• IAM
• DevOps