AI Software Engineer - SME

About the role

Dark Wolf constructs and deploys data management and analytics solutions for the defense and intelligence communities. We’re proud to boast a world-class engineering team that thrives on rolling up their sleeves to solve your mission’s biggest challenges. Dark Wolf is seeking an AI Software Engineer Subject Matter Expert (SME) to architect, lead, and execute complex, high-assurance AI/ML engineering initiatives. As the primary technical authority, you will drive enterprise-scale architectural vision, design novel AI systems, oversee multi-cloud and air-gapped infrastructure, and advise key government stakeholders on high-impact AI strategies. Location: Chantilly/Herndon, VA.

Responsibilities

• Architect and lead the end-to-end technical execution of enterprise-scale AI software systems.
• Serve as the principal technical authority on novel AI frameworks, LLMs, and high-assurance ML capabilities.
• Define enterprise security standards and mandate security scanning tool integrations across complex systems.
• Set the strategic vision for multi-cloud, hybrid, and air-gapped infrastructure design.
• Interface directly with executive and agency leadership to align software engineering roadmaps with high-priority mission objectives.

Qualifications

• Clearance: Must be a US Citizen holding an active TS/SCI security clearance with an active Full-Scope Polygraph.
• Education: Bachelor's degree in Computer Science, Information Systems, Engineering, or a related technical field (Master’s degree preferred).
• Experience: 15+ years of software engineering experience.
• Desired Qualifications:
  • Advanced Certifications: AWS Certified Solutions Architect, Certified Information Systems Security Professional (CISSP), Kubernetes Certified Administrator (CKA), or specialized AI/ML certifications.
  • Deep understanding of high-assurance software engineering, zero-trust architecture, and classified deployment constraints.

Skills

• AI Frameworks: Enterprise AI ecosystem architecture, framework governance, and strategic platform selection.
• AI Models: Architecting novel model architectures, fine-tuning strategies, and deployment of LLMs in air-gapped environments.
• Machine Learning: Enterprise MLOps architecture, governance, lifecycle management, and scalable ML operations.