AI Product Lead

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

THE ROLE

JLL's Project & Development Services (PDS) organization is deploying AI across its construction and real estate project delivery workflows, and we're looking for someone who builds things because they can't help themselves. You stay current on AI not because it's your job but because you find it genuinely fascinating. You have side projects. You've broken things trying to understand how they work.

You have opinions about context windows.

In this role you'll design and build AI-powered solutions that PDS project managers use on a daily basis. You won't be building large standalone applications. You'll be building the AI layer: the logic, the context, the pipelines, and the integrations that make a general-purpose AI model behave like a domain expert in construction project delivery. The specific form that takes will evolve as the technology evolves, and you'll be expected to evolve with it.

WHAT THIS JOB INVOLVES

Designing and Building AI Solutions - Your primary output will be AI-powered workflow tools: structured, versioned, documented solutions that guide an AI model through specific PDS tasks like reviewing a GC payment application, abstracting a commercial lease, or analyzing a construction schedule for risk. These are not one-off prompts. They have defined inputs, structured outputs, behavioral guardrails, and quality checks. You'll build them, test them against real documents, iterate based on failure modes, and maintain them over time.

The architecture may shift as better patterns emerge; your responsibility is the outcome, not a specific implementation approach.

Scripting and System Integration - Most AI solutions require supporting code: scripts that parse input documents, validate outputs, call external APIs, format deliverables, or move data between systems. You'll write that code, frequently Python, and connect AI tools to JLL's enterprise environment using standard integration patterns.

AI Solution Design - You'll think carefully about what information an AI model needs to do a job well and what it doesn't. You'll decompose real-world workflows into problems AI can solve reliably, design the inputs and outputs, and write instructions that produce consistent results across varied documents and edge cases. You'll need to understand how structure and context affect output quality and develop intuition for where AI performs well versus where it needs guardrails or human review.

Testing and Quality Engineering - You'll build test harnesses, run solutions against diverse real-world documents, and treat output quality as an engineering problem. You'll define what "good" looks like before you start building, not after.

Stakeholder Collaboration - You'll work directly with PDS project managers to understand workflows well enough to build for them accurately, capturing the edge cases practitioners know intuitively and translating messy real-world processes into clean, buildable specifications. You'll also communicate clearly about what AI can and cannot do reliably, setting appropriate expectations while demonstrating real capability.

DESIRED QUALIFICATIONS

Candidates who bring most of the following will be strongly considered. This is a genuinely new field though. The expectation isn't that you arrive knowing everything on this list; it's that you're the kind of person who would be pursuing most of it on your own regardless.

Technical Foundation

* Familiarity with Python: file I/O, data parsing, scripting for automation

* Familiarity with REST APIs and enterprise integration patterns

* Comfort with version control (Git), structured file management, and markdown-based documentation

* Exposure to enterprise security and governance requirements for AI systems (data residency, access controls, audit logging)

AI Solution Development

* Hands-on experience building with large language models: designing system prompts, managing context, structuring inputs and outputs, optimizing for token efficiency, handling failure cases

* Understanding of how context design affects LLM output quality and reliability

* Familiarity with retrieval-augmented generation (RAG) concepts and why grounding AI in specific knowledge matters for enterprise use cases

* Exposure to agentic patterns, tool use, and multi-step AI reasoning

* Curiosity about emerging AI infrastructure patterns (orchestration frameworks, context protocols, model APIs) and how they enable AI to connect to…