DevSecOps Engineering Intern

Alpha Omega

10h•Onsite

About The Position

The DevSecOps Engineering Intern will support the development and maintenance of secure, AI-driven application development pipelines within Alpha Omega's Continuum AF Secure track. This role focuses on integrating security-by-design practices and AI-powered automation into application stacks used for federal government solutions. The intern will collaborate with the internal engineering team to support AIOps-driven security automation, AI-assisted vulnerability detection, intelligent compliance monitoring, and compliance-aligned development practices. This internship provides hands-on experience with secure software development, AI-augmented DevSecOps pipelines, AI coding assistants, AI-driven threat modeling, and federal security frameworks such as NIST 800-53, FedRAMP, and the NIST AI Risk Management Framework (AI RMF). This role also helps strengthen Alpha Omega's ability to maintain a production-ready, AI-enhanced secure stack used for technical proposal demonstrations, client solutions, and rapid prototype development.

Requirements

Proficiency in at least one backend language such as: Python, Node.js, Java
Familiarity with frontend frameworks (React preferred)
Foundational knowledge of secure coding practices and OWASP Top 10
Understanding of static and dynamic application security testing (SAST/DAST)
Experience using AI coding assistants (e.g., GitHub Copilot, Cursor, Amazon CodeWhisperer) for code generation, debugging, and refactoring
Basic understanding of prompt engineering for security-focused AI tools and LLM-based code analysis
Basic experience with cloud platforms (AWS or Azure preferred)
Familiarity with containerization tools such as Docker or Kubernetes
Exposure to AI/ML or LLM-based tools used for automation/coding/operations
Familiarity with AI/ML concepts including supervised/unsupervised learning, NLP, and LLM architectures as they apply to security automation
Exposure to AIOps platforms or concepts such as AI-driven monitoring, intelligent alerting, predictive analytics for infrastructure, and automated remediation
Understanding of AI security risks including prompt injection, model evasion, data poisoning, and adversarial machine learning
Experience or coursework involving AI-powered coding/security tools
Familiarity with LLM-based code review and vulnerability analysis workflows
Familiarity with federal security frameworks such as: NIST 800-53, FedRAMP, FISMA
Academic or project-based experience is acceptable.

Nice To Haves

Currently pursuing or recently completed a Bachelor’s or Master’s degree in: Computer Science, Software Engineering, Human-Centered Design, Information Technology, or a related technical field

Responsibilities

Support development and enhancement of AI-augmented DevSecOps pipelines within the Continuum AF Secure track
Assist with integration of AI-assisted SAST/DAST tools (e.g., GitHub Copilot Autofix, Semgrep with LLM triage, Snyk AI, SonarQube AI) into internal development pipelines
Leverage AI coding assistants (e.g., GitHub Copilot, Amazon CodeWhisperer, Tabnine) to accelerate secure code generation while validating AI-generated code for security compliance
Support AIOps initiatives including AI-driven log analysis, anomaly detection, automated incident response, and intelligent alerting within CI/CD environments
Assist in developing and fine-tuning AI/ML models for automated vulnerability classification, threat prioritization, and security pattern recognition
Evaluate and implement AI-powered security scanning tools that use machine learning to reduce false positives and improve detection accuracy
Contribute to development of secure application stack components used for demonstrations, prototypes, and proposal support
Help document NIST 800-53, FedRAMP, and NIST AI RMF-aligned security control implementation patterns for internal reuse across engagements
Support AI security governance by helping assess risks associated with AI/ML model deployment, including adversarial attack surfaces, data poisoning, and model integrity
Support automation efforts to reduce manual security review processes through AI-driven pipeline enhancements and intelligent workflow orchestration
Assist with AI-powered vulnerability detection, triage, and remediation efforts using LLM-based analysis
Collaborate with engineering teams to maintain secure coding practices, AI-safe development standards, and compliance-ready infrastructure patterns
Participate in technical discussions related to secure SDLC, compliance automation, cloud security, and responsible AI integration in federal environments