Security Control Assessor/Representatives at darkwolfsolutions

Compensation

$135,000 – $150,000/yr

Setup: Remote, Hybrid
Location: Washington, District of Columbia
Type: Full-time
Level: not_specified
Posted: Jul 30, 2026

Description

Dark Wolf Solutions is seeking Security Control Assessors/Representatives (SCA/Rs) to lead security control assessments across high-priority projects. The role involves evaluating security controls for cutting-edge AI/LLM technologies across multiple classification levels, focusing on cybersecurity engineering, cloud architecture, and DevSecOps prototyping. Candidates should excel in fast-paced DevSecOps environments, understand AWS cloud security, and aim to shape the cybersecurity posture of next-generation DoD AI capabilities.

Required Skills

Cybersecurity Engineering
Cloud Architecture
DevSecOps Prototyping
AWS Cloud Security
NIST SP 800-53 (Rev. 4/5)
DoD 8570/8140 IAM Level II or Level III Certification
RMF Artifacts
AWS Cloud Services Operational Understanding
Government GRC Repository Experience

Additional Information

Required Previous Experiences: 3–5+ years of experience conducting security control assessments, compliance testing, or A&A/RMF activities for DoD or federal information systems.
Preferred Qualifications: Hands-on experience mapping security controls to the NIST AI Risk Management Framework (AI RMF), the OWASP Top 10 for LLM Applications, or the DoD Responsible AI (RAI) Guidelines.
Familiarity evaluating secure design patterns for autonomous AI Agents (e.g., tool-calling permissions, sandboxing agent execution environments, prompt boundaries, and ReAct/LangGraph architectures).
Experience assessing cloud-managed AI ecosystems and foundation model platforms (e.g., AWS Bedrock, AWS SageMaker, Hugging Face Enterprise, or self-hosted open-source models).
Understanding of data protection, access controls, and boundary security for RAG pipelines and vector databases (e.g., OpenSearch Vector Engine, Pinecone, Milvus, or PostgreSQL pgvector).
Familiarity evaluating risks unique to LLMs—including prompt injection, data poisoning, model inversion, insecure output handling, and open-source supply chain vulnerabilities in AI libraries (PyTorch, LangChain, LlamaIndex).
Exposure to LLM guardrail platforms, evaluation frameworks, or AI security tools (e.g., Promptfoo, Garak, Giskard, NeMo Guardrails) used to test model robustness and output safety.
Experience with cATO methodologies, Infrastructure as Code (IaC) templates (Terraform, CloudFormation), and container security (AWS EKS/ECS, Docker).
Active AWS Certifications (e.g., AWS Certified Security – Specialty or AWS Certified Solutions Architect).
Background or familiarity with offensive security, penetration testing