CACI Behavioral Interview for AI Engineer — Mission-Focused Delivery in Secure Government Environments

This behavioral interview at CACI evaluates how an AI Engineer operates in mission-critical, security-first settings that support U.S. federal customers. Expect a STAR-driven conversation led by a hiring manager and a technical lead (occasionally joined by a program manager or security representative). The discussion probes integrity, judgment, collaboration, and delivery discipline when working under contract constraints, evolving requirements, and secure/air‑gapped environments. What it covers (focus areas): 1) Mission and ethics alignment: Demonstrating CACI’s "mission-first" mindset, protecting sensitive information, handling pressure to cut corners, and making risk-aware tradeoffs that uphold compliance and security. 2) Stakeholder and customer engagement: Partnering with government sponsors, managing requirement churn and scope within contract boundaries, setting expectations, and communicating status and risks clearly to non-technical officials. 3) Delivery in secure environments: Executing ML/AI projects with limited tooling/connectivity, working with restricted datasets, documenting decisions for audits, and supporting authorization processes typical of secure programs. 4) Responsible and explainable AI behaviors: Anticipating bias, model risk, and operational impacts; designing human-in-the-loop workflows; and explaining model behavior to operators and mission leaders. 5) Teaming and leadership in a matrix organization: Collaborating with systems engineers, data engineers, and analysts; mentoring junior staff; resolving conflict; and coordinating across dispersed teams and cleared facilities. 6) Communication and documentation: Producing clear, defensible documentation (e.g., model cards, test reports, deployment runbooks) and tailoring message depth for technical and executive/government audiences. 7) Delivery accountability: Owning outcomes, navigating ambiguity, adapting plans under changing priorities, and learning from retrospectives and incident/drift scenarios. Format and signals: 45–60 minutes, mostly scenario prompts with deep follow-ups. Interviewers look for specific, quantifiable outcomes, evidence of security-minded decision-making, and respect for process/quality gates common in federal programs. Recommended preparation: Prepare 5–7 STAR stories that show (a) delivering AI/ML in constrained or high-compliance settings, (b) handling ambiguous or changing requirements with a government customer, (c) remediating model bias or drift with measurable impact, (d) negotiating scope/schedule tradeoffs, (e) documenting for audits/authorizations, and (f) leading through conflict while maintaining trust.