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Jones Lang LaSalle

Advises on and manages commercial real estate

Regional Workplace Program Manager

Program ManagementPosted on Nov 11
Full-timeSingaporeNo salary listed

About the Job

Requirements

  • Typically 5+ years of relevant experience across software engineering, technical program delivery, automation, or AI enablement, including hands-on AI solution delivery and cross-functional adoption initiatives; candidates with fewer years and strong evidence of equivalent impact are encouraged to apply.
  • Demonstrated ability to build, test, and deploy useful technical solutions or reusable workflows, with clear documentation and evidence of personal contribution from concept through implementation.
  • Practical use of AI-assisted coding tools including Codex, and ability to review, test, and troubleshoot generated code. Proficiency in Python or another modern programming language is advantageous.
  • Working knowledge of APIs, integrations, version control, software testing, and product-management fundamentals including requirements, prioritisation, user testing, releases, and maintenance.
  • Experience leading programs through planning, delivery, and follow-through, including stakeholder coordination, participant support, and outcome measurement.
  • Understanding of workplace operations and employee experience, with ability to translate user needs into practical requirements and intuitive tools.
  • Communication, facilitation, and coaching skills, with evidence of helping non-technical colleagues sustain new ways of working after training; ability to support small-group and remote learning and explain technical choices plainly.
  • Sound judgment about data quality, access, privacy, reliability, failure modes, human review, and AI limitations, including ability to recognise when engineering, security, or another specialist must take ownership.
  • A relevant degree or equivalent practical experience is welcome.

Responsibilities

  • Own the AI Fluency and Enablement Program plan, rollout, stakeholder coordination, and ongoing learning rhythm; align with program sponsors and regional counterparts to reuse proven materials and share builds across regions.
  • Support operational teams to complete Fieldwork, preferably through facilitated sessions using the REW ChatGPT Fluency Field Guide; coordinate facilitators, learning time, practical participant support, and defined share-out sessions.
  • Assess readiness and resolve account, approved tool-access, and training gaps with responsible parties; agree support coverage and review cadence with account and operations leaders, adapting delivery to local service scope, language, time zone, and team maturity.
  • Assess participant retention and application of learning in workplace use cases; capture reviewed examples showing the problem, output, and checks, and use feedback and delivery evidence to improve the program.
  • Identify operational pain points and personally build selected AI applications, lightweight agents, and automations connecting approved tools such as Slack, service-request systems, dashboards, and internal platforms.
  • Take solutions through requirements, prototyping, coding, integration, testing, deployment, documentation, and ongoing improvement; involve qualified engineering support for complex production work.
  • Partner with workplace teams and employees on workplace assistants, meeting-room interactions, and wayfinding tools, adapting proven solutions across sites where appropriate.
  • Integrate software with workplace devices such as sensors, cameras, touch displays, and room-status hardware when required by an approved use case.
  • Troubleshoot software and hardware interfaces with relevant teams and suppliers, including privacy, security, and operational review before deployment.
  • Lead additional workplace programs supporting adoption of evolving OpenAI technology, including evaluating and integrating emerging hardware and robotics where relevant and approved; partner with technology, security, privacy, safety, and operations teams on readiness and deployment.
  • Educate teams on the difference between a one-time prompt, a reusable skill, and an automated workflow; define inputs, steps, outputs, checks, and stop rules before adding a trigger or agent.
  • Coach teams on setting limits for delegated tasks, defining agent roles, and managing handoffs; require approved sources and permissions, representative tests, human review, monitoring, escalation, and recovery proportionate to risk.
  • Maintain an organised library of approved methods, skills, and tools with named owners, documentation, version history, and a feedback route; reuse proven work where permissions and regional needs allow.
  • Review workflow quality and learning gaps, communicate relevant feature changes, and coordinate with technology, data, and security specialists on integrations, source reliability, and production risks.
  • Maintain a prioritised roadmap with outcomes, milestones, dependencies, costs, and delivery owners; communicate progress, unresolved risks, and blockers.
  • Arrange approved specialist support with clear deliverables and acceptance criteria, retaining end-to-end delivery accountability within agreed authority.
  • Confirm service-owner acceptance and assign named owners for maintenance, backup arrangements, support, and fallback procedures; test failure paths before scaling.
  • Track reliability, sustained adoption, and improvements in workplace experience or operational performance.
  • Agree measures and baselines with relevant stakeholders; use existing service records and representative, aggregated team evidence of accepted work, errors, corrections, reuse, and independent capability.
  • Distinguish prototypes from dependable workflows and generated answers from completed physical work; treat participation as an early adoption signal rather than delivery evidence.
  • Assess whether teams can define outcomes, test work, and know when to stop or escalate; do not use individual prompt counts, logins, or AI usage quotas.
  • Balance program leadership and participant enablement with hands-on development and technical problem-solving, building shared ownership and team capability rather than dependence on one person.
  • Work closely with regional and account leadership, workplace operations, learning and development, IT, data, security, privacy, engineering, service owners, and approved external partners.
  • Follow required approvals and data-handling standards; keep automation within approved scope with human review and a practical fallback, and obtain appropriate permission and safeguards for sensitive process recordings.
  • Start with capability and access assessment, a few recurring jobs, and learning and support routines; demonstrate a tested workflow with an owner and acceptance evidence, and match coverage and resources to service demand before expanding.

Desired Qualifications

  • Hardware integration, front-end development, dashboard design, and multi-site rollout experience would be useful.

About the Company

Jones Lang LaSalle

Advises on and manages commercial real estate

Jones Lang LaSalle, known as JLL, advises property owners, occupiers and investors on commercial real estate worldwide. Its teams broker leases and property sales, arrange financing, value assets, manage buildings and workplaces, and oversee construction projects for businesses, institutions and governments. LaSalle Investment Management invests client capital in private property and real estate securities, while JLL Technologies develops workplace software and data tools. The company earns commissions, service fees, investment management fees and software revenue, with some client operating costs reimbursed through contracts. JLL took its current form through a 1999 merger and is headquartered in Chicago, Illinois.

Company size
10,001+
Company stage
IPO
Headquarters
Chicago, IL
Founded
1999

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