Technology/AI Lead

JLL•Northborough, MA

18h•$132,000 - $150,000•Hybrid

About The Position

The Technology/AI Lead designs, builds, and deploys AI-enabled solutions and process improvements across an account spanning facilities management, project management, finance, and operations. This hands-on role combines EAM/CMMS and reliability engineering expertise with AI-powered automation and analytics to identify opportunities to reduce costs and downtime and improve service delivery across a geographically distributed utility portfolio. The Lead turns operational pain points into solutions, advances them from proof of concept to monitored production deployment, and measures outcomes against KPIs agreed with client leadership. The role improves workflows, develops predictive maintenance and asset lifecycle solutions, builds data pipelines and dashboards, and supports responsible AI governance, security, and compliance in a regulated utility environment. It also measures ROI, presents business cases and progress to account and client stakeholders, and trains and supports facilities and project teams adopting new tools. The position combines in-office collaboration with remote-work flexibility and travel to client sites as needed. It is on-site in Albany, New York, or Northborough, Massachusetts. Visa sponsorship is not provided; candidates must be authorized to work in the United States without sponsorship.

Requirements

Bachelor's degree in mechanical engineering, industrial engineering, facilities management, data science, business, or a related field.
At least 7 years of experience in enterprise asset management, maintenance and reliability, or facilities operations, including technology implementation.
Expert-level experience with one or more EAM/CMMS platforms, including configuration, data migration, and multi-site deployment.
Demonstrated experience applying AI/ML, predictive analytics, or automation to maintenance and asset management workflows.
Strong grounding in predictive and preventive maintenance, reliability-centered maintenance, FMEA, root cause analysis, and asset criticality analysis.
Working proficiency in SQL and Python, plus dashboarding tools such as Power BI or Cognos.
Experience with asset data governance, data quality, and ETL in an enterprise environment.
Demonstrated process improvement experience, such as process mapping, workflow redesign, or Lean/Six Sigma methods.
Ability to manage cross-functional work in a client-facing environment, lead training and adoption, and explain technical concepts clearly to non-technical stakeholders.
Must be authorized to work in the United States without visa sponsorship.

Nice To Haves

Experience in utility, energy, petroleum, industrial, or critical infrastructure environments.
Experience with IoT-enabled building systems or condition monitoring; energy optimization experience is a plus.
Familiarity with ISO 55001 asset management standards and asset lifecycle or capital planning.
Experience with Databricks or integrating EAM data into a lakehouse or semantic layer.
Exposure to LLM-based tools such as RAG, AI agents, or natural-language query applied to maintenance or asset data.
Background in change management.
PMP, CFM, CMRP, Lean Six Sigma, or EAM platform certifications.

Responsibilities

Assess workflows across facilities, project management, finance, and operations to identify and prioritize opportunities for AI, automation, and process redesign.
Map current-state processes with on-site teams, identify bottlenecks and manual effort, and design streamlined future-state workflows.
Maintain an innovation pipeline for evaluating, piloting, and scaling relevant AI, IoT, and automation capabilities.
Build and deploy AI-enabled solutions for predictive maintenance, capital project forecasting, and automated reporting.
Architect AI-powered automation in EAM/CMMS platforms, including predictive analytics, automated diagnostics, and intelligent reporting.
Design and optimize reliability programs, including reliability-centered maintenance, FMEA, root cause analysis, asset criticality analysis, and risk-based preventive maintenance intervals.
Develop asset lifecycle and capital planning models using maintenance and condition data to support repair-versus-replace and capital deferral decisions.
Develop proofs of concept and move validated prototypes into production-grade, monitored solutions.
Enhance asset data governance standards, including equipment coding, asset hierarchies, and data quality controls.
Build ETL processes and data links between EAM/CMMS, financial, and project systems using SQL and Python, including structures for unstructured data such as work orders and inspection reports.
Design executive KPI dashboards in Power BI or similar tools for PM compliance, MTBF/MTTR, downtime trends, backlog, and cost performance.
Partner with JLL data engineering teams on Databricks, defining business logic, metric definitions, and asset data requirements for the governed semantic layer and natural-language query tools.
Apply responsible AI governance frameworks covering data quality, lineage, model performance monitoring, and evaluation of AI-generated answers against trusted data.
Implement access controls such as row- and column-level security and masking to meet data classification, privacy, cybersecurity, and client technology standards.
Track and report initiative performance against KPIs, including cost savings, downtime reduction, forecast accuracy, and user adoption.
Present business cases, progress updates, and innovation roadmaps to JLL account leadership and client stakeholders.
Train facilities and project teams on AI-enabled tools, develop training materials, run workshops, translate technical capabilities into practical workflows, and provide rollout support.
Collaborate with JLL technology, data, and innovation teams and the client's facilities, operations, and other stakeholders.