System Engineering & Intelligence

About Nscale

Nscale is the vertically integrated AI cloud engineered for AI. We own and operate the full stack — energy, data centres, GPU superclusters, orchestration, and AI services — delivering high-performance infrastructure to AI-native companies, enterprises, and governments across Europe and the US. We are deploying GPU capacity at hyperscale, operating some of the densest, most advanced AI infrastructure in the world.

At Nscale, our Data Centre Systems Engineering team designs the systems, data, and intelligence that let us build and operate AI data centres at a speed and density traditional operating models were never designed for. We thrive on a culture of relentless innovation, ownership, and accountability, where every team member takes pride in their work and drives it with excellence and urgency. As an Nscaler, you'll build trust through openness and transparency, where everyone is inspired to do their best work. If you join our team, you'll be contributing to building the technology that powers the future.

About the Role

Nscale's operational systems (BMS, DCIM, EPMS/SCADA, CMMS/EAM, ITSM, fabric and GPU telemetry) and equipment data were procured independently. Each works well on its own, but together they give overlapping and sometimes contradictory views of the same assets.

As Senior Director, System Engineering & Intelligence, you will bring that data centre insight together. You'll own the equipment digital thread — from product definition and procurement through commissioning and handover into maintenance — along with the telemetry and analytics foundation that lets the Nscale Operational Systems (NOS) act on our physical estate with confidence.

The goal is data centres that are measurably self-aware and progressively self-operating, where a site is declared ready based on evidence rather than judgement.

What You'll be Doing

- Set strategy and investment: own a costed, sequenced multi-year roadmap and investment portfolio across CMMS/EAM, PIM/PLM, integration, telemetry, and the data platform, starting with an assessment of which systems should be consolidated, retained, replaced, or newly instrumented.
- Own the asset master and governance: establish one canonical identity for every asset, along with taxonomy, hierarchy, configuration, and BOM standards, ensuring every critical data object has one system of record.
- Lead cleansing and validation-at-source programmes so governed lifecycle data replaces spreadsheets.
- Build the telemetry foundation: deliver a unified asset and topology model from grid connection down to GPU and network port, with OT/IT ingest (Modbus, BACnet, OPC UA, MQTT, Redfish, DCGM) and IEC 62443-aligned segmentation.
- Publish data-quality scores and agree single, shared definitions for PUE, WUE, capacity, stranded power, and availability. - Make maintenance and reliability trustworthy: own the CMMS/EAM configuration baseline (criticality, PM strategy, spares, warranties) and controlled SOP/MOP/EOP libraries. - Map condition data deterministically to assets so predictive maintenance raises work orders, not just reports. - Move NOS intelligence toward autonomy: build anomaly detection, remaining-useful-life prediction, and thermal and power optimisation. - Progress control loops through shadow, recommend-only, human-approved, and bounded-autonomous stages, each with explicit safety envelopes and engineering sign-off. - Assure site readiness: turn commissioning (L1–L5) into data, set acceptance gates for handover, and score readiness quantitatively per hall and per site. - Benchmark readiness across the portfolio so defects found on one build are designed out of the next. - Act as the senior escalation point across Engineering, Procurement, Construction, Operations, Reliability, Platform, Finance, and HSE, and enforce supplier and OFCI data deliverables. - Build and develop a high-performing team spanning lifecycle engineering, data science, and data engineering.