Job Requirements
Annapolis Junction, MD
Intel Agency (NSA, CIA, FBI, etc) Full Scope Polygraph
Mid Level Career (5+ yrs experience)
Salary not specified
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Job Description
Candidates must already possess an active Top Secret/SCI w/ Full Scope Polygraph to be considered.
Summary:
• Deploy and manage containerized AI/ML services using Docker and Kubernetes in on-premises infrastructure environments.
• Implement comprehensive system observability and monitoring solutions utilizing Prometheus and Grafana.
• Build and maintain robust CI/CD pipelines, GitOps workflows, and infrastructure-as-code to support automated and reliable platform engineering.
Qualifications & Compensation:
• Degree: Technical bachelor's degree or equivalent experience
• Years of experience: 12+ years
• Total Compensation: $346k+ yearly
Job Description:
• Deploy and manage containerized AI and machine learning services using Kubernetes in production on-premises environments.
• Implement observability and monitoring frameworks using Prometheus and Grafana to track system health, performance, and usage metrics.
• Build and maintain CI/CD pipelines and infrastructure-as-code to facilitate automated deployment of platform services.
• Design and implement resilient infrastructure patterns to ensure high availability and stability for mission-critical capabilities.
• Collaborate with data scientists, systems administrators, and mission stakeholders to translate operational reliability requirements into platform engineering solutions.
• Maintain configuration management and deployment automation utilizing GitOps workflows and version control.
About SYSTOLIC:
SYSTOLIC is dedicated to giving our employees the best possible company experience so that they can focus on providing outstanding support to their customer’s mission. Our company is founded on integrity, enthusiasm, and a relentless commitment to supporting the Intelligence Community. You can learn more about us and submit an application to be considered against our current and future openings at https://systolic.com.
To learn about our compensation ranges, visit our Pay Transparency page at: https://systolic.com/pay-transparency
Summary:
• Deploy and manage containerized AI/ML services using Docker and Kubernetes in on-premises infrastructure environments.
• Implement comprehensive system observability and monitoring solutions utilizing Prometheus and Grafana.
• Build and maintain robust CI/CD pipelines, GitOps workflows, and infrastructure-as-code to support automated and reliable platform engineering.
Qualifications & Compensation:
• Degree: Technical bachelor's degree or equivalent experience
• Years of experience: 12+ years
• Total Compensation: $346k+ yearly
Job Description:
• Deploy and manage containerized AI and machine learning services using Kubernetes in production on-premises environments.
• Implement observability and monitoring frameworks using Prometheus and Grafana to track system health, performance, and usage metrics.
• Build and maintain CI/CD pipelines and infrastructure-as-code to facilitate automated deployment of platform services.
• Design and implement resilient infrastructure patterns to ensure high availability and stability for mission-critical capabilities.
• Collaborate with data scientists, systems administrators, and mission stakeholders to translate operational reliability requirements into platform engineering solutions.
• Maintain configuration management and deployment automation utilizing GitOps workflows and version control.
About SYSTOLIC:
SYSTOLIC is dedicated to giving our employees the best possible company experience so that they can focus on providing outstanding support to their customer’s mission. Our company is founded on integrity, enthusiasm, and a relentless commitment to supporting the Intelligence Community. You can learn more about us and submit an application to be considered against our current and future openings at https://systolic.com.
To learn about our compensation ranges, visit our Pay Transparency page at: https://systolic.com/pay-transparency
group id: 10527119