DevSecOps Architect (focus on AI/ML Security)

Job Description:

Job Description -

Submit candidates with their DL, Visa Copy, LinkedIn.

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