PM asks you to build a personalized ranking system for a fintech app’s home page that orders products (e.g., high-yield savings, credit cards, personal loans, brokerage). Describe an end-to-end design: (1) Precisely define the primary objective and guardrail metrics—be explicit about a revenue- or CLV-weighted conversion objective while constraining ineligible impressions, underwriting risk, and customer harm; specify how you’d weight click, application start, approval, and funded events with delay. (2) Detail data and features: user eligibility/suitability signals (e.g., geo, KYC completion, credit profile availability), short- and long-term behavior, session context, product attributes, real-time events; call out what cannot be used due to fairness/compliance. (3) Model architecture: candidate generation vs ranking, objective choice (listwise e.g., LambdaRank/soft-NDCG vs pairwise), calibration, and a constrained re-ranker that enforces eligibility, product quotas, and per-user suitability; include latency/throughput budgets. (4) Exploration vs exploitation with safety: propose a contextual bandit or epsilon-greedy layer; define safe exploration that never violates eligibility or risk guardrails; handle cold-start users/products. (5) Offline evaluation: time-based splits, leakage checks, and metrics (NDCG@K, ERR, CVR@K, expected revenue@K) with calibration and stability checks; address position bias using IPS/SNIPS or randomized swaps. (6) Online experimentation: A/B plan with pre-registration of success and guardrail metrics (approval rate, complaint rate, bad-rate proxy, drop-off in critical flows), sample size, duration, and sequential testing risk; discuss interleaving vs full-funnel tests and how to attribute downstream approvals with long delays. (7) Monitoring and feedback loops: drift detection, eligibility bugs, fairness dashboards, and rollback criteria; propose a fallback ranking when signals are sparse. Provide concrete thresholds, formulas, and trade-offs you would set for launch.