
AECOM AI Engineer Case Interview: Smart Infrastructure Predictive Analytics for Water Networks
This case mirrors AECOM’s project-centric, multidisciplinary style and assesses how you translate real-world infrastructure constraints into an end‑to‑end AI solution that a public- or private-sector client can adopt. You’ll be given a 1–2 page brief and a lightweight schema excerpt. Scenario: A municipal water utility engages AECOM to reduce main breaks and non‑revenue water by 15% within 12 months. You must scope and design a predictive system that prioritizes leak detection and pipe replacements while fitting into the client’s existing asset management and field operations. What you’ll cover during the session: 1) Problem framing and assumptions (5–10 min): Clarify client goals, KPIs (e.g., breaks/month, NRW%), success metrics, schedule, budget, and constraints typical of AECOM projects (safety, ESG, equity in service delivery, regulatory). 2) Data diligence (10–15 min): Assess data realities common in brownfield infrastructure—GIS/asset registry, work orders/CMMS, SCADA pressure/flow, soil/traffic/weather layers, and survey/inspection reports; propose data quality triage and feature plan. 3) Modeling approach (10 min): Justify techniques for failure risk ranking (e.g., gradient boosting/bayesian survival), uncertainty handling, explainability for public stakeholders, and policy‑aware decision thresholds. 4) Architecture & integration (10–15 min): Sketch cloud/on‑prem or air‑gapped patterns; streaming vs batch; MLOps (versioning, drift, retraining), integration with BIM/GIS and field crew workflows; security and auditability. 5) Plan & change management (10–15 min): Phased rollout (pilot → scale), cost/benefit, risk register (data gaps, model bias, operational disruption) with mitigations; tie to ESG outcomes and equitable service improvements. 6) Communication (ongoing): Structure a concise, client‑ready narrative and visuals suitable for mixed technical/non‑technical audiences. Evaluation signals aligned to AECOM culture: client impact orientation, systems thinking across disciplines, practical engineering judgment, inclusivity and stakeholder empathy, risk/safety mindset, and clear trade‑off reasoning under schedule/budget pressures.
70 minutes
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About This Interview
Interview Type
PRODUCT SENSE
Difficulty Level
4/5
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