
DevSecOps Engineer
Primary stack
Nice to have's
Job description
About the Position
We are seeking a DevSecOps Engineer to design, implement, and secure cloud infrastructure for the Office of Naval Research (ONR) Data & Analytics environment. The successful candidate will lead the technical requirements identified in PWS Section 4.5 – Technical Infrastructure and Platform Support, including infrastructure automation, Continuous Integration/Continuous Deployment (CI/CD), cloud security, container orchestration, Infrastructure-as-Code (IaC), and operational sustainment of the FedRAMP High and DoD Impact Level 5 (IL5) cloud environment.
Responsibilities
- Design, deploy, automate, and maintain secure cloud infrastructure supporting mission applications and analytics platforms.
- Develop, implement, and manage automated CI/CD pipelines using tools such as Jenkins, GitLab CI/CD, or equivalent technologies.
- Build and maintain Infrastructure-as-Code (IaC) solutions utilizing Terraform, Ansible, or similar automation frameworks.
- Implement and manage containerized application environments using Docker, Kubernetes, or comparable orchestration platforms.
- Configure, monitor, and optimize cloud-native security services including Web Application Firewalls (WAF), Intrusion Detection/Prevention Systems (IDS/IPS), endpoint protection, and Security Information and Event Management (SIEM) platforms.
- Analyze security logs, audit events, vulnerability reports, and SIEM alerts to identify threats, recommend mitigation strategies, and support incident response activities.
- Support continuous Authority to Operate (ATO) compliance through configuration management, security patching, vulnerability remediation, and security documentation.
- Implement secure API management, identity and access management (IAM), secrets management, encryption, and least-privilege access controls.
- Develop automated monitoring, logging, alerting, and operational dashboards to improve system reliability and performance.
- Support deployment and operationalization of AI/ML platforms and services, including cloud-native machine learning environments.
- Collaborate with software developers, data engineers, and cloud architects to integrate security throughout the software development lifecycle (SDLC).
- Ensure cloud infrastructure complies with DoD cybersecurity policies, FedRAMP High requirements, DoD IL5 controls, and applicable security frameworks.
- Support system scalability, resilience, disaster recovery, and operational continuity.
- Document system architectures, deployment procedures, security configurations, and operational processes.
Requirements
- Seven (7) years of combined experience in DevSecOps, DevOps, SecOps, or MLOps engineering and development.
- Five (5) years of hands-on experience designing, securing, implementing, and maintaining cloud environments, preferably within AWS GovCloud, Azure Government, or similarly regulated cloud environments.
- Demonstrated experience supporting cloud environments operating under FedRAMP High, DoD Impact Level 5 (IL5), or comparable federal security requirements.
- Verifiable experience on at least two (2) projects designing, implementing, and managing Continuous Integration/Continuous Deployment (CI/CD) pipelines using Jenkins, GitLab CI/CD, Azure DevOps, GitHub Actions, or equivalent technologies.
- Demonstrated expertise implementing and managing containerization and orchestration technologies, including Docker, Kubernetes, OpenShift, or similar platforms.
- Hands-on experience implementing Infrastructure-as-Code (IaC) using Terraform, Ansible, CloudFormation, or comparable automation tools.
- Experience configuring and administering cloud-native security capabilities including Web Application Firewalls (WAF), Intrusion Detection/Prevention Systems (IDS/IPS), Security Information and Event Management (SIEM), vulnerability management, and cloud security monitoring solutions.
- Verifiable experience analyzing security logs, SIEM data, audit records, and threat intelligence to identify vulnerabilities, investigate security events, and implement corrective actions.
- Experience implementing secure Identity and Access Management (IAM), encryption, secrets management, and Zero Trust security principles.
- Experience supporting automated monitoring, logging, system performance optimization, and operational sustainment of enterprise cloud platforms.
- Strong understanding of DevSecOps best practices, secure software development lifecycle (SSDLC), and cloud security architectures.
Qualifications
- Active Secret security clearance (required).
- Current DoD 8140 (or legacy DoD 8570) cybersecurity certification applicable to privileged access functions (e.g., Security+, CySA+, CASP+, CISSP, CCSP).
- AWS Certified DevOps Engineer – Professional, AWS Security Specialty, Azure DevOps Engineer Expert, Certified Kubernetes Administrator (CKA), or comparable cloud certifications.
- Experience supporting AI/ML environments, including AWS SageMaker, Azure Machine Learning, or similar platforms.
- Experience with streaming data technologies such as Apache Kafka, AWS Kinesis, or equivalent event-driven architectures.
- Familiarity with NIST RMF, DISA STIGs, FedRAMP, Zero Trust Architecture, and DoD cybersecurity compliance requirements.
We Offer
- Competitive salary
- Full-Time employment
- Opportunity to work in Arlington, Virginia, United States
About the Company
Occam Solutions is a leading provider of technology and consulting services to the federal government, specializing in cloud infrastructure, cybersecurity, and data analytics. We are committed to delivering innovative solutions that meet the highest standards of security and performance.
© Freiheit. This job description was sourced from the employer's public career page. TheJob is not the employer — we index the posting and route candidates to the source. All content rights and hiring decisions belong to the employer.
Rjvgfybz занимается научными исследованиями в области общегоискусственного интеллекта. Существенным элементом нашей работы является создание компактных представлений произвольных данных, которые поступают агенту через его сенсоры. Мы создали теорию, с помощью которой такие представления могут быть найдены (эффективная аппроксимация универсальной индукции Соломонова с помощью инкрементного/иерархического сжатия). Чтобы проверить её пользу, мы планируем создать систему, которая сможет мыслить о средах ограниченной сложности и обучаться широкому спектру простых концепций, используя эти компактные представления.
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