Job Description:
Job Description -
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Senior DevSecOps Architect (focus on AI/ML Security)
Client: Peraton
Work Location: East Coast Remote, (Candidates located in Raleigh, NC, Washington, DC or Eagan, MN will get preference)
Duration: 6+ months
Clearance: Public Trust Clearance
Skills: ci/cd, ai/ml to automate threat detection, code reviews, sast/dast, securing ai workflows, intelligence automation, pac policy as a code, threat modeling and red team, developer empowerment, devops and infrastructure, security tooling, ai/ml, programming, observability
Must Have:
Job Description
Description:
As a Senior DevSecOps Architect, you will lead the evolution of our software delivery lifecycle by embedding security into every stage of the CI/CD pipeline. This role is specifically focused on leveraging Artificial Intelligence to automate threat detection, optimize code reviews, and secure AI-native applications. You will be responsible for building a robust, self-healing DevSecOps ecosystem that balances rapid deployment with uncompromising security standards.
Key Responsibilities
* AI-Enhanced Pipeline Security: Architect and maintain automated CI/CD pipelines that utilize AI/ML models for static and dynamic analysis (SAST/DAST) to identify complex vulnerabilities that traditional rule-based tools miss.
* Securing AI Workflows: Design security frameworks for the end-to-end AI lifecycle, including securing data ingestion, protecting model weights, and implementing "Guardrail" architectures for Large Language Models (LLMs).
* Intelligent Automation: Develop AI-driven orchestration (SOAR) to automate the triage and remediation of security findings, reducing manual overhead for engineering teams.
* Policy as Code (PaC): Implement enterprise-wide governance using tools like Open Policy Agent (OPA) to enforce security compliance automatically across multi-cloud environments.
* Threat Modeling & Red Teaming: Conduct advanced threat modeling for cloud-native applications, specifically accounting for AI-specific attack vectors like model inversion or data poisoning.
* Developer Empowerment: Create self-service security tools and "Golden Paths" that allow developers to deploy securely without friction, fostering a proactive security culture.
Technical Qualifications:
Category - DevOps & Infrastructure
Requirements - Expert-level experience with Kubernetes, Terraform/Pulumi, and cloud platforms (AWS, Azure, or Google Cloud Platform).
Category - Security Tooling
Requirements - Mastery of integrating SAST, DAST, SCA, and Secret Scanning into GitHub Actions, GitLab CI, or Jenkins.
Category - AI/ML Security
Requirements - Hands-on experience securing MLOps pipelines and implementing security for AI-integrated APIs.
Category - Programming
Requirements - Proficiency in Python, Go, or Bash for building custom security automation and AI-driven scripts.
Category - Observability
Requirements - Deep knowledge of eBPF, Prometheus, and AI-powered logging/monitoring tools (e.g., Dynatrace or Datadog).
Experience
* Experience: 10+ years in IT, with at least 5 years dedicated to DevSecOps leadership in high-scale environments.
* AI Expertise: Proven track record of implementing AI/ML to solve operational or security challenges.
* Education: A degree from an accredited College/University in the applicable field of services is required. If the individual's degree is not in the applicable field then four additional years of related experience is required..
* Soft Skills: Strong ability to bridge the gap between "Speed of DevOps" and "Rigors of Security" while communicating clearly with executive leadership.
Core Objectives
* Shift Left: Achieve 90% automated security coverage across all production-bound code.
* Mean Time to Remediate (MTTR): Utilize AI to reduce vulnerability remediation time by 40% within the first year.
* Innovation: Stay at the forefront of the "AI for Security" and "Security for AI" movements
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