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L\&D Product Lead, AI
Job Summary:
As a key member of the global Learning \& Development team, you will work closely with the Global Head of Sustainability and AI Learning, the AI Product Engagement team and wider Learning and Development stakeholders (i.e. design and delivery and project management team members). You will be responsible for the coordination and delivery of strategically important AI learning programs that drive company\-wide engagement in JLL’s AI tools, and achieve the company’s ambitious AI training targets.
Primary Stakeholders: Global Head of Sustainability and AI Learning, Director of AI Product Engagement, AI Lead Trainer.
Job Details:
- Execute and maintain JLL’s AI learning strategy in alignment with global AI learning goals.
- Deliver on goals through coordination and collaboration with learning specialists responsible for delivering the portfolio of global learning programs.
- Acting as an expert and staying abreast of rapidly changing global AI and AI learning trends, regulations, risks, and commercial opportunities to ensure these are reflected in learning programs and delivered to the entire enterprise.
- Use creative and new ways of designing learning solutions as well as “tried and true” solutions like instructor\-led training (ILT), digital learning, on\-the\-job learning \& job aids, special project assignments and formal external study to meet learning needs, considering local cultural, language and other needs.
- Support the Global Head of Sustainability and AI Learning to drive continuous improvement of the global AI learning program portfolio, to ensure provision of a tailored offering for different business lines, accounts, and teams with up to date, forward looking, business critical content and messaging.
- Monitor adoption, delivery, and success measures along the way – using relationships, data and feedback to measure and report on impact and effectiveness.
- Work with communication and marketing for collateral and promotion of AI learning product and programs.
Key Skills:
- Strategic thinking: able to combine industry, institutional and subject matter knowledge with input from key stakeholders to define, maintain and adapt the global AI learning strategy in alignment with the global AI engagement strategy.
- Agile: open to and comfortable with change, combined with the ability to thrive in a fast paced and rapidly evolving environment.
- Stakeholder management: skilled at listening and communicating in an effective manner; providing regular updates on progress against strategic agenda, including key accomplishments, challenges and support needed; managing expectations proactively to ensure stakeholder delight.
- Relationship management: highly adept at initiating, developing, and maintaining positive relationships with a wide range of stakeholders across the business and beyond, ranging across all levels within the organization.
- Subject matter expertise: bring a broad but detailed level of knowledge and understanding of the learning and development landscape with a particular focus on AI.
- Impactful collaborator: lead by example to drive and inspire a team of learning specialists focused on delivering best in class and impact focused learning programming, that delivers against the ambitions of the AI learning and engagement strategies.
- Communication: a highly engaging communicator who can translate complex subjects and communicate these in a manner that is digestible and understandable to a lay audience.
- Needs analysis: use strong consulting skills to fully understand business needs and ensure L\&D resources are focused on the right work / solutions.
- Data\-driven storytelling: leverage data to tell effective stories about progress or opportunities with respect to the strategic L\&D roadmap; articulate the business impact of L\&D’s work.
- Facilitation \& design: design engaging and impactful learning, if applicable (may include decks for in\-person training, webinars, or e\-learnings); Facilitate engaging training to accelerate skills and shift mindsets and behaviours across the business.
- Key technical skills: Strong PowerPoint, Word, Excel and SharePoint skills as well as relevant AI tools (i.e. Generative AI, Agentic AI and L\&D specific AI tools)
This position does not provide visa sponsorship. Candidates must be authorized to work in the United States without sponsorship.
Estimated compensation for this position:
130,000\.00 – 140,000\.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:
On\-site –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.
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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 Los Angeles Fair Chance Initiative for Hiring Ordinance, JLL will consider for employment all qualified Applicants, including those with Criminal Histories, in a manner consistent with the requirements of applicable state and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance.
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.
Salary Context
This $130K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($135K) sits 25% below the category median. Disclosed range: $130K to $140K.
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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