AI Product Engineer

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 business line is building production AI capabilities for construction and real estate project delivery, and we need someone who thinks at a systems level. You're a solution developer, but you’re also an engineer who builds the infrastructure that makes AI solutions reliable, scalable, and maintainable.

In this role you'll be responsible for the engineering layer underneath our AI-powered workflow tools: the extraction scripts, validation frameworks, output schemas, integration connectors, and quality harnesses that turn a capable AI model into a dependable production tool. You'll set engineering standards, make architectural decisions, and be the person others come to when a pipeline is misbehaving in a way nobody can explain.

WHAT THIS JOB INVOLVES

Working With Real-World Data Enterprise AI solutions are only as good as the data they operate on, and real-world business data is rarely clean, consistent, or structured the way a model would prefer. You'll develop deep familiarity with the information landscape of construction and real estate project delivery, understanding what data exists, where it lives, what form it takes, and what has to happen before an AI model can do something useful with it.

Output Design and Quality Assurance You'll design the structured output contracts that govern what AI solutions produce and build the validation logic that enforces them. When a solution produces unexpected output or degrades silently on an unusual document, you'll own the detection and recovery logic. You'll define what production-ready looks like before building begins, run solutions against diverse real-world document sets, and maintain quality as the underlying models and input corpus evolve over time.

Enterprise System Integration You'll connect AI solutions to JLL's enterprise environment using REST APIs, Microsoft Graph, SharePoint, OneDrive, and other standard integration surfaces. You'll handle authentication lifecycle, retry logic, rate limits, and the realities of operating inside an enterprise network with real access controls. You'll design integrations that are resilient and maintainable, not just functional in a demo environment.

Agentic Architecture and MCP Integration As AI solutions grow more capable, you'll design and build multi-step reasoning pipelines that connect models to enterprise tools and data through the Model Context Protocol and similar agentic infrastructure. You'll think carefully about how to structure tool availability, manage context across steps, and build agent workflows that are reliable and auditable rather than unpredictable. You'll stay current on how this space is evolving and bring informed opinions about when agentic patterns are the right approach and when they aren't.

Platform Engineering and Standards As the AI solution portfolio grows, you'll establish and maintain the engineering patterns others follow: packaging conventions, versioning, configuration management, logging, and error handling. You'll write internal tooling that makes building new solutions faster and less error-prone, and you'll make architectural decisions that hold up as the team and codebase scale.

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.

Engineering Foundation

Strong Python proficiency: data parsing, file I/O, schema validation, subprocess management, packaging, and test authoring (pytest or similar)

Solid understanding of REST API design and consumption, including auth patterns (OAuth, API keys, token refresh), pagination, and error handling

Comfort with document parsing libraries: PyMuPDF, python-docx, openpyxl, pandas, and equivalent tools for common enterprise file formats

Experience with Git-based development workflows: branching, versioning, code review, and structured release management

Familiarity with enterprise integration surfaces, particularly Microsoft 365 (SharePoint, OneDrive, Graph API)

AI Engineering

Hands-on experience building the code layer around LLM APIs: structuring prompts programmatically, managing token budgets, parsing and validating model outputs, and handling failure cases gracefully

Understanding of how structured context, schema-constrained outputs, and validation pipelines improve AI solution reliability in production

Familiarity with document chunking, embedding workflows, and retrieval patterns (RAG), including the tradeoffs between retrieval approaches for enterprise document types

Exposure to agentic patterns, multi-step reasoning pipelines, and tool use via MCP or similar protocols

Quality and Reliability

Experience building test infrastructure for systems with probabilistic outputs: evaluation frameworks, regression suites, benchmark datasets

Comfort defining "correct" programmatically for outputs that don't have a single right answer, and building scoring logic that reflects domain standards

Instinct for failure modes: silent errors, schema drift, edge-case documents, and model-version-induced regressions

Domain Familiarity

Experience in or meaningful exposure to construction, commercial real estate, or professional services environments is a plus

Prior work in a technical role at a professional services firm, PropTech company, or enterprise software organization is relevant background

Mindset

You’ve built something from scratch specifically to understand how it worked

You're comfortable making principled decisions in the absence of established conventions, and you document those decisions so the next person understands the reasoning

You hold your technical opinions firmly enough to be useful and loosely enough to update them

You're energized by fields where the tooling is still being invented and you can influence how it develops

This position does not provide visa sponsorship. Candidates must be authorized to work in the United States without sponsorship.

Expected compensation for this position:

• 246,120.00 – 246,120.00 USD per year
• Final compensation packages are determined by various considerations including but not limited to candidate qualifications, location, market conditions, and internal considerations.

Location:

• Remote –Chicago, IL
• If this job description resonates with you, we encourage you to apply, even if you don’t meet all the requirements. We’re interested in getting to know you and what you bring to the table!

Personalized benefits that support personal well-being and growth:

JLL recognizes the impact that the workplace can have on your wellness, so we offer a supportive culture and comprehensive benefits package that prioritizes mental, physical and emotional health. Some of these benefits include:

• •401(k) plan with matching company contributions
• •Comprehensive Medical, Dental & Vision Care
• •Paid parental leave at 100% of salary
• •Paid Time Off and Company Holidays
• •Early access to earned wages through Daily Pay

At JLL, we harness the power of artificial intelligence (AI) to efficiently accelerate meaningful connections between candidates and opportunities. Using AI capabilities, we analyze your application for relevant skills, experien