SAIC AI Engineer Case Interview: Secure Mission AI System Integration

This SAIC-specific case simulates being the AI engineer on a delivery team building a secure, production-grade AI capability for a defense/intelligence customer. The scenario reflects common SAIC interviews: a panel-style, solution-oriented discussion emphasizing systems integration, compliance in classified or GovCloud environments, and measurable mission impact. Format and flow (70 minutes): - 5 min: Mission brief (interviewer reads a 1–2 page prompt on an ISR/operations use case with multi-sensor data and analyst workflows). - 20 min: Candidate-led solution framing (requirements, assumptions, risks, success metrics, stakeholders, phasing). - 20 min: Architecture and ML approach (whiteboard verbally): • Data pipeline: ingest from EO/IR/FMVs, text reports, and geospatial metadata; labeling strategy (active learning, weak supervision, synthetic data); lineage and audit. • Models: justify object detection/segmentation (e.g., YOLO/DETR/Mask R-CNN) and NLP triage (e.g., transformer-based classifiers); late vs. early fusion for multi-INT; explainability (Grad-CAM/SHAP) for analyst trust. • MLOps in restricted environments: artifact versioning (MLflow/DVC), containerization, CI/CD to air-gapped clusters, promotion gates, drift detection, rollback, model cards. • Deployment: edge acceleration (TensorRT/quantization/pruning), inference servers (Triton/TorchServe), latency/throughput targets, resource constraints (Jetson vs. data center GPUs). • Security/compliance: RMF/NIST 800-53 controls, STIG-hardened images, SBOM/signing (SLSA), secrets management, cross-domain solutions, data minimization, auditing, ATO path. - 15 min: Deep dive and tradeoffs: cost vs. performance, accuracy vs. explainability, GovCloud vs. on-prem IL5/IL6, human-in-the-loop design, fail-safe behavior. - 10 min: Delivery plan: 12-week MVP roadmap, test/acceptance plan (precision/recall, false alarm rate, latency SLOs), operational runbook, risk register with mitigations. What interviewers look for (aligned to SAIC culture): - Mission-first pragmatism: translating vague sponsor needs into measurable outcomes and phased value delivery. - Systems integration mindset: blending COTS/GOTS/open-source with SAIC/partner components; clear interfaces and data contracts. - Secure MLOps expertise: operating in classified/air-gapped or GovCloud environments with strong governance, reproducibility, and auditability. - Communication and teaming: clear rationale, stakeholder management, and ability to collaborate with engineers, security, and mission SMEs. Artifacts expected during the case: - High-level architecture (data flows, control planes, security boundaries). - Metrics plan (e.g., precision/recall targets by class, latency budgets, drift thresholds). - Risk/assumption list (label scarcity, CDS latency, model brittleness) with mitigations. - Brief deployment/runbook notes (monitoring, alerts, rollback, retraining cadence). Sample prompts SAIC interviewers may use: - "How would you stand up a compliant MLOps pipeline for IL5 with air-gapped promotion?" - "Trade DETR vs. YOLO for maritime detection on edge GPUs—what do you pick and why, given a 100 ms/frame budget?" - "Design a drift detection plan when you cannot stream data off enclave; what signals and thresholds do you use?" - "Outline your ATO path and artifacts for the model and pipeline components." - "How do you integrate analyst feedback to reduce false positives without risking data spillage across domains?" Evaluation rubric (scored 1–5 each): - Requirements and mission alignment - Technical depth in ML/AI and data engineering - Secure MLOps and compliance literacy - Architecture quality and tradeoff reasoning - Communication, clarity, and stakeholder awareness