AI Consultant, Development & Asset Lifecyle, Global

Evolution Cloud Services (EVOCS)•Denver, CO

4d•$200,000 - $215,000•Remote

About The Position

This is a rare opportunity to serve as a trusted AI advisor and discovery catalyst embedded within a world-class Data Center Development & Asset Lifecycle function. You'll act as the critical bridge between business operations and development of meaningful AI solutions, translating real operational needs into validated, high-impact opportunities for AI-driven transformation. This isn't a program management or solution-building role; it's about discovering, validating, and preparing the right AI opportunities so they're ready for rapid prototyping. You'll work across the full data center asset lifecycle, connecting critical handoff points between IT AI, Market Development, Engineering & Construction, and Operations & Facilities to identify improvement opportunities to drive AI solutions. Success requires equal parts business acumen, technical fluency, and the interpersonal skills to build trust with stakeholders at every level of enthusiasm for AI.

Requirements

5–7+ years of experience across multiple phases of the data center or infrastructure asset lifecycle, development, construction, and/or operations exposure is key.
Practical AI knowledge: familiarity with digital twins, predictive analytics, cross-system data integration, and lifecycle optimization as they apply to infrastructure asset management.
Lifecycle fluency: you understand how data center assets progress from market identification through design, construction, commissioning, operations, and capital reinvestment, and appreciate the information challenges at each transition point.
Systems toolkit awareness: experience with project management platforms, BIM tools, construction management software, CMMS/EAM systems, and operational monitoring platforms.
Cross-functional credibility and handoff expertise: a track record of working across organizational boundaries and solving process breakdowns at the seams where information loss or misalignment creates downstream inefficiencies.
Strong systems thinking: you understand how decisions in one lifecycle phase cascade through subsequent phases and impact long-term asset performance.
Comfort with ambiguity and genuine curiosity: you thrive in the spaces between established functions and are fascinated by how different teams experience the same asset at different lifecycle stages.

Responsibilities

Embed across the asset lifecycle, conducting structured discovery sessions and regular listening tours, to build trusted relationships with development managers, program directors, transition teams, and asset management professionals, becoming the go-to resource for AI opportunity identification at organizational handoff points.
Translate lifecycle challenges into preliminary AI use cases, assessing feasibility against data availability across development databases, project management systems, BIM models, and operational platforms.
Build a champion network spanning multiple functional boundaries, identifying transition managers, program leads, and asset management professionals who feel the pain points at organizational seams.
Validate AI opportunities through rapid assessment of business value potential, evaluating impact on transition quality, information continuity, lifecycle cost optimization, and time-to-operational-readiness for new facilities.
Develop detailed intake documentation, capturing cross-functional context, data lineage, integration complexity, lifecycle impact, and success criteria, to set up downstream teams for success.
Facilitate cross-functional workshops bringing together stakeholders from different lifecycle phases to envision a continuous digital thread from development concept through operational performance.
Partner with lifecycle stakeholders to develop success criteria that span functional boundaries, measuring end-to-end lifecycle outcomes rather than single-function improvements.
Serve as a cross-functional connector, identifying patterns where insights from one lifecycle phase (e.g., construction learnings) can meaningfully inform another (e.g., operational procedures or future development standards).
Support change management across organizational boundaries, helping teams understand how lifecycle AI tools benefit the entire value chain.
Maintain deep understanding of the full data center lifecycle, market entry through decommissioning, monitoring emerging AI capabilities including digital twins, predictive asset management, and cross-system data integration to continuously surface new opportunities.
Champion responsible AI principles across lifecycle applications, ensuring solutions maintain appropriate governance, access controls, and accountability structures.
Contribute methodology improvements and track discovery pipeline health, reporting on cross-functional conversion metrics.