AI Platform and DevSecOps Engineer
$100kJohns Hopkins Applied Physics Lab
Description Are you searching for important work at the intersection of National Security Analysis and Artificial Intelligence? Are you passionate about deploying, sustaining, and securing AI-enabled software? If so, we're looking for someone like you to join our team at APL, working at the forefront of operationalizing artificial intelligence in national security environments. In this role, you will bridge the gap between AI development and real‑world deployment—ensuring that advanced capabilities, including large language models and supporting services, are delivered securely, reliably, and at scale to mission environments. Working within the Advanced Mission Modeling and AI Group (JAM) in the National Security Analysis Department (NSAD), you will play a critical role in ensuring that AI capabilities are not just developed—but successfully fielded and maintained across DoD and Combatant Command infrastructures. As an AI Platform and DevSecOps Engineer… Your primary responsibility will be to develop, maintain, and continuously update a secure library of containerized software and supporting stacks, along with implementing automated pipelines for container builds, testing, and validation and ensuring compatibility across multiple operating systems and architectures. You will design, deploy, and maintain Kubernetes-based infrastructure, maintain well‑documented, reproducible deployment configurations, and optimize performance and reliability across constrained environments. You will develop processes to continuously scan containers and dependencies for vulnerabilities and remediate findings, ensuring compliance with evolving STIG requirements and security controls and integrating security best practices into CI/CD pipelines. You will navigate deployments across DoD and IC systems, adapt deployment strategies to meet ATO and accreditation constraints, and troubleshoot environment‑specific deployment issues. Qualifications You meet our minimum qualifications for the job if you… Possess a Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering or a closely related technical field. Have at least three years of relevant experience deploying software across Windows and Linux. Have experience with containerization technologies (Docker, Podman) and orchestration (Kubernetes). Have familiarity with CI/CD pipelines and DevSecOps practices. Have experience with vulnerability scanning and remediation. Have proficiency in Bash, or similar scripting languages. Possess strong problem‑solving skills in constrained environments. Are able to obtain an Interim Secret level security clearance by your start date and can ultimately obtain a TS/SCI level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship. Preferred Qualifications Have experience deploying to classified or air‑gapped environments. Possess familiarity with DoD DevSecOps processes. Have experience supporting AI/ML or LLM‑based systems. Have knowledge of container security hardening and STIGs. Have experience with multi‑architecture builds. Have experience with Kubernetes in restricted environments. All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law. APL is committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities. If you require a reasonable accommodation to participate in any part of the hiring process, please contact View email address on click.appcast.io. Minimum Rate $100,000 Annually Maximum Rate $245,000 Annually #J-18808-Ljbffr Johns Hopkins Applied Physics Lab
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