AI Solution Developer

Role Description

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.

Qualifications

• 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)
• 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 enterprise tools and data
• Experience working in or familiarity with the construction, commercial real estate, or professional services industry
• Experience in a product management, engineering, data science, or other relevant technical role
• You build things outside of work because you find the problems interesting
• You follow AI model and tooling developments because you want to, not because someone asked you to
• You're comfortable in problem spaces where the best practices are still being written, and you find that energizing
• When the technology shifts, you shift with it

Requirements

• This position does not provide visa sponsorship. Candidates must be authorized to work in the United States without sponsorship.
• Estimated compensation for this position: 200,000.00 – 230,000.00 USD per year
• This range is an estimate and actual compensation may differ. Final compensation packages are determined by various considerations including but not limited to candidate qualifications, location, market conditions, and internal considerations.
• Location: Remote – Charlotte, NC, Chicago, IL, Dallas, TX, New York, NY

Benefits

• 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