Director, Technology Product Management

JLL empowers you to shape a brighter way .   Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology for our clients. We are committed to hiring the best, most talented people and empowering them to thrive, grow meaningful careers and to find a place where they belong.  Whether you’ve got deep experience in commercial real estate, skilled trades or technology, or you’re looking to apply your relevant experience to a new industry, join our team as we help shape a brighter way forward.    ABOUT JLL TECHNOLOGIES JLL Technologies (JLLT) is the technology and data science engine of JLL, one of the world's leading commercial real estate services firms. JLLT builds intelligent, connected, and sustainable workplace solutions powered by proprietary platforms, data products, and AI. The Workplace Management (WPM) Transformation Program is redefining how JLL and its clients operate facilities — embedding real-time insights and AI-powered automation into day-to-day workplace operations.

We need a product manager who builds things. Not someone who writes strategy decks and hands them over — someone who opens a laptop, fires up a model, strings together an agent, and ships something by end of day. This Director of Product Management role owns the product for Workplace OS, JLL's next-generation workplace management platform, and will be measured by what gets built and whether it works.   You will define features, write specs, experiment with AI tools, prototype workflows, and work shoulder-to-shoulder with engineers and designers to turn ideas into product. You are energized by the pace of AI development and have a habit of testing new models, frameworks, and tools before anyone tells you to. You hold strong opinions about what makes a great product experience but know that the right answer often comes from trying things — not from more meetings.

Ship Product Own the end-to-end product lifecycle for core Workplace OS capabilities — from discovery through launch and iteration. Write clear, detailed product specs and acceptance criteria that engineers can actually build from, including edge cases, error states, and AI output handling. Sit in engineering standups, participate in sprint reviews, and stay close enough to the code to catch problems before they ship. Manage the backlog with discipline: prioritize ruthlessly, cut scope when necessary, and keep the team focused on what matters most. Experiment Aggressively with AI Prototype new AI-powered features yourself — using tools like Claude, GPT-4o, Cursor, or whatever the current best tool is for the job. Test and evaluate LLMs, agentic frameworks, and automation tools to inform build vs. buy decisions with real evidence. Design and iterate on agentic workflows for workplace operations: safety conversations, compliance tracking, participation nudging, facilities incident routing. Define the human-in-the-loop escalation paths, confidence thresholds, and guardrails that make AI features safe to put in front of real users. Stay current on the AI landscape — not as a hobby, but as a professional obligation. You should know what shipped last week and whether it matters for what we're building. Drive Clarity from Ambiguity Translate fuzzy business problems and operational pain points into precise product requirements and testable hypotheses. Work directly with Facilities Managers, Account Directors, and HSSE leads to understand what actually breaks in the field — and design for it. Define success metrics for every feature before it ships, and own the post-launch review to determine whether the feature worked. Identify open questions early, flag them loudly, and drive to resolution rather than letting them become blockers mid-sprint. Collaborate Across the Stack Partner with engineering leads, data scientists, and ML engineers as a peer — not a client. Understand the