Why This Role Stands Out
This hybrid role offers exciting opportunities to build and automate secure, scalable infrastructure for cutting-edge medical research, leveraging your Python, Kubernetes, and Linux expertise. You'll thrive if you enjoy complex problem-solving and developing innovative solutions in a regulated environment, and this contract-to-hire position is perfect for advancing your career in open-source technologies. Apply today to contribute to impactful work and grow your skills!
Quick Overview
Job Description
Position: Sr Open Source Engineer/Kubernetes/Linux/Python
Type: contract to hire
Duration: 12 months
Location: Pleasanton, CA (Remote)
Overview
We are seeking a highly skilled and self-directed Open-Source Engineer to support a modern medical research and laboratory environment. This role focuses on building, automating, and operating secure, scalable, and compliant infrastructure for medical imaging, laboratory systems, data processing, and research workflows.
The ideal candidate has deep Python and Linux automation expertise, operates comfortably across Kubernetes, virtualized, and mixed OS environments, and has experience supporting regulated research platforms in HIPAAcompliant settings.
Key Responsibilities
- Develop and maintain production-grade Python and shell-based automation, including complex workflow orchestration using Apache Airflow, secure data transfers (SFTP), and reliability improvements.
- Design, deploy, and operate onpremises Kubernetes environments (Rancher RKE2 preferred), including Helm-based deployments and secrets management with HashiCorp Vault.
- Administer Linux/UNIX systems (RHEL 9.x focus), including OS patching, lifecycle maintenance, security hardening, and automation with Ansible.
- Create and maintain standardized VM provisioning pipelines and repeatable system builds; support mixed Linux and Windows environments where required.
- Develop and support medical imaging workflows, including DICOM query/retrieve, image pulls, storage, and automated deidentification, using platforms such as Orthanc, DCMTK, or CTP.
- Support laboratory and research platforms, including LIMS infrastructure, laboratory data ingestion workflows, and associated Windows batch and integration scripts.
- Install, test, upgrade, and maintain research and enterprise applications across test and production environments (e.g., LIMS, JBOSS-based systems, Appian, data analysis tools).
- Provision and optimize storage access using NFS and S3, integrating with highperformance platforms such as Weka, VAST, and EMC.
- Manage and support Jupyter, R, and RStudio environments, including package management and researcher workflows.
- Implement GitLab-based CI/CD pipelines, infrastructure-as-code, and version-controlled operational runbooks.
- Ensure systems meet HIPAA, CAP, and ISO compliance requirements, including documentation, change control, audit evidence, and traceability.
- Provide senior-tier Linux/UNIX support to both technical teams and research users; mentor staff and document operational best practices.
- Act as a proactive self-starter, managing multiple concurrent efforts and adapting to evolving research and laboratory needs.
Required Qualifications
- Advanced Python automation experience, supported by strong Bash/shell scripting skills.
- Deep hands-on experience administering Linux systems (RHEL preferred), including patching, security, and lifecycle management.
- Proven experience managing onprem Kubernetes platforms and Helm-based application deployments.
- Strong understanding of Apache Airflow, HashiCorp Vault, and workflow-driven automation.
- Experience supporting DICOM-based medical or research imaging systems.
- Working knowledge of NFS and S3 storage access models and high-performance storage environments.
- Demonstrated experience supporting regulated research or healthcare systems, including audit and compliance readiness.
- Proficiency with GitLab, including repositories, CI/CD pipelines, and automation workflows.
- Ability to communicate clearly, document effectively, and collaborate across multidisciplinary teams.
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