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About This Role
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.
Estimated compensation for this position:
246,120\.00 – 246,120\.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 –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 may 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, experiences, and qualifications to generate valuable insights about how your unique profile aligns with the specific requirements of the role you're pursuing.
*JLL Privacy Notice*
Jones Lang LaSalle (JLL), together with its subsidiaries and affiliates, is a leading global provider of real estate and investment management services. We take our responsibility to protect the personal information provided to us seriously. Generally the personal information we collect from you are for the purposes of processing in connection with JLL’s recruitment process. We endeavour to keep your personal information secure with appropriate level of security and keep for as long as we need it for legitimate business or legal reasons. We will then delete it safely and securely.
For more information about how JLL processes your personal data, please view our Candidate Privacy Statement.
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For candidates in the United States, please see a full copy of our Equal Employment Opportunity policy here.
Jones Lang LaSalle (“JLL”) is an Equal Opportunity Employer and is committed to working with and providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the employment process – including the online application and/or overall selection process – you may email us at [email protected]. This email is only to request an accommodation. Please direct any other general recruiting inquiries to our Contact Us page \> I want to work for JLL.
Pursuant to the Arizona Civil Rights Act, criminal convictions are not an absolute bar to employment.
Pursuant to Illinois Law, applicants are not obligated to disclose sealed or expunged records of conviction or arrest.
Pursuant to Columbia, SC ordinance, this position is subject to a background check for any convictions directly related to its duties and responsibilities. Only job\-related convictions will be considered and will not automatically disqualify the candidate.
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Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Accepting applications on an ongoing basis until candidate identified.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At JLL, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
JLL AI Hiring
JLL has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Chicago, IL, US, Austin, TX, US. Compensation range: $75K - $140K.
Location Context
AI roles in Chicago pay a median of $201,225 across 312 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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