
Capgemini AI Engineer Case Interview — End‑to‑End Responsible AI Solution Design
What this covers: A client‑facing, end‑to‑end AI engineering case mirroring Capgemini’s consulting‑led delivery model. You’ll scope an enterprise use case, design a cloud‑native architecture, define an evaluation/monitoring plan, and communicate trade‑offs to both technical and business stakeholders. The case emphasizes Capgemini hallmarks: client value, structured problem‑solving, delivery pragmatism, and Responsible AI. Format (facilitator brief): 1) Client context (5 min): Interviewer reads a short brief for a global client (e.g., retail, FS, manufacturing) seeking a GenAI/ML solution under compliance and cost constraints. 2) Problem structuring (10 min): You clarify objectives, users, KPIs, risks, and success criteria; show MECE thinking and client empathy. 3) Solution design (30 min): Whiteboard an architecture for data ingestion/processing, model strategy (classical ML vs. LLMs or hybrid RAG), feature or retrieval layer, inference, observability, and CI/CD for ML (MLOps). Address multi‑cloud realities (Azure/AWS/GCP) common at Capgemini clients, data residency (EU/US), PII handling, and performance/cost targets. 4) Responsible AI & governance (10 min): Propose safety and compliance controls (privacy, bias testing, explainability, content safety, audit trails), change‑management, and human‑in‑the‑loop. 5) Business narrative & delivery plan (5 min): Communicate ROI, phased roadmap (pilot→MVP→scale), risk/mitigation, and stakeholder alignment. 6) Q&A (5 min): Defend trade‑offs and discuss sustainability and operational readiness. Representative prompt: “A multinational insurer wants to deploy a multilingual AI assistant to deflect 20% of call volume within 6 months while meeting EU data‑residency and SOC 2 requirements. Historical call transcripts and knowledge articles are available; the CRM is Salesforce; budget is capped at €250k for MVP.” What strong answers include (Capgemini‑specific focus areas): - Client‑centric scoping: Clear problem statement, users, SLAs, and measurable KPIs (e.g., deflection rate, CSAT, p95 latency, cost/interaction, hallucination rate). - Architecture depth: Data pipelines (batch/stream), RAG store design (chunking/embeddings), model selection rationale (open‑source vs. managed APIs), prompt orchestration/guardrails, scalable inference, and blue/green or canary rollouts. - MLOps rigor: Feature/retrieval versioning, experiment tracking, automated evaluation harnesses, CI/CD with model registry, drift detection, observability (latency, quality, safety, cost), rollback strategy. - Responsible AI: Bias/fairness checks, explainability where required, prompt injection mitigation, PII redaction, access control, auditability, and policy alignment; discuss human‑override for sensitive flows. - Cost & sustainability: Cost modeling (tokens/throughput, vector DB, egress), capacity estimates, and citing sustainable choices (efficient models, autoscaling, caching). - Consulting DNA: Clear storylining for executives, pragmatic trade‑offs, and alignment with Capgemini values (team spirit, trust, modesty) in collaboration and communication. Evaluation rubric (how you’re assessed): - Structure & communication (clarity, MECE, executive summary) - Technical design quality (sound, scalable, secure) - Responsible AI & compliance (fit for regulated enterprises) - Delivery pragmatism (phased plan, risk/mitigation, ops readiness) - Business impact (KPIs, ROI narrative, stakeholder alignment) Artifacts expected in-session: A labeled architecture diagram, KPI/metric plan, risk register highlights, and a brief phased roadmap.
8 minutes
Practice with our AI-powered interview system to improve your skills.
About This Interview
Interview Type
PRODUCT SENSE
Difficulty Level
4/5
Interview Tips
• Research the company thoroughly
• Practice common questions
• Prepare your STAR method responses
• Dress appropriately for the role