Peraton is seeking a Director to lead the administration and user enablement of Peraton[x], our enterprise generative AI and agentic analytics platform. This leader is accountable for how the platform is configured, governed, and accessed, and for making sure every program that uses it can do so successfully. The Director leads a multidisciplinary team of system administrators, AI analysts, business analysts, and knowledge workers who onboard users, build reusable solutions, curate knowledge, and drive adoption across Peraton. The role connects end users, program leadership, and platform engineering. It turns business needs into well-administered platform capabilities and measurable adoption outcomes.
Job Responsibilities
Platform Administration & Governance
- Lead all Peraton[x] system administration, including user and program provisioning, role-based access and data-tier management, environment configuration, and platform settings.
- Set and enforce administrative standards, policies, and procedures for access requests, onboarding and offboarding, project and workspace management, and knowledge-base (cortex) governance.
- Oversee release readiness and change management from the administrator and end-user side, including communicating platform changes, new features, and impacts to users.
- Run the service desk and tiered user support model: ticket intake, triage, escalation to engineering, and resolution tracking against service-level targets.
- Manage AI usage, cost, and license administration: track consumption and spend by program, attribute costs, and enforce usage policies.
- Maintain administrative documentation, runbooks, and audit-ready records of access, configuration, and change.
User Enablement & Adoption
- Own the Peraton[x] enablement strategy, including role-based onboarding, training curricula, office hours, user guides, and playbooks for new and experienced users.
- Build and lead a community of practice and a power-user/champion network across programs to spread best practices and grow adoption.
- Direct AI and business analysts in designing reusable skills, flows, workflows, dashboards, and templates that solve recurring program problems and lower the barrier to entry.
- Partner with programs to identify high-value use cases, guide pilots, and scale successful solutions across the enterprise.
- Measure adoption, engagement, and value using usage analytics and user feedback, and use the results to target training and improvements.
Knowledge Management
- Lead knowledge workers in curating, organizing, and governing platform content, including enrolled documents, ontologies, knowledge graphs, and reference materials.
- Set standards for content quality, metadata, classification, and lifecycle (review, archive, retire) so knowledge stays accurate and discoverable.
- Maintain a central knowledge base of FAQs, how-to content, and lessons learned for users and administrators.
Team Leadership & Talent Development
- Lead, mentor, and grow a cross-functional team of system administrators, AI analysts, business analysts, and knowledge workers.
- Define roles, career paths, performance goals, and development plans, including AI literacy, prompt and agent design, and platform administration skills.
- Plan staffing and hiring, and assign team members to programs based on enablement and support demand.
- Build a culture of service, accountability, collaboration, and continuous learning.
Stakeholder Engagement
- Serve as the primary point of contact for program leaders and business units on platform access, onboarding, support, and adoption.
- Gather and prioritize user and program requirements and represent them to product and engineering teams.
- Report adoption, user satisfaction, support performance, cost, and value metrics to senior leadership.
- Manage the administration and enablement budget, vendor relationships, and licensing.
Security, Compliance & Responsible AI
- Make sure platform administration complies with Peraton policy, the Acceptable Use Policy, and relevant frameworks (e.g., NIST 800-171, CMMC, FedRAMP).
- Enforce least-privilege access, data classification, periodic access reviews, and audit-evidence practices.
- Promote responsible AI use through training, human-in-the-loop review, output verification, and governed approval workflows.
