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About This Role
Build your best future with the Johnson Controls team
Johnson Controls, a global leader in thermal management, mission-critical building systems, energy efficiency, and decarbonization, helps customers use energy more productively, reduce carbon emissions, and operate with the precision and resilience required in rapidly expanding industries such as data centers, healthcare, pharmaceuticals, advanced manufacturing, and higher education.
For more than 140 years, Johnson Controls has delivered performance where it matters most. Backed by advanced technology, lifecycle services, and an industry-leading field organization, we elevate customer performance, turn goals into real-world results, and help move society forward.
What We Offer
- Competitive salary
- Paid vacation, holidays, and sick time
- Comprehensive benefits package including 401(k), medical, dental, and vision care
- On-the-job and cross-training opportunities
- Encouraging and collaborative team environment
- Dedication to safety through our Zero Harm policy
What You Will Do
Johnson Controls is seeking a Product AI Manager to lead the strategy, development, and delivery of AI-enabled products and solutions that drive business value across the enterprise.
In this role, you will partner with business leaders, product teams, data scientists, and software engineers to identify high-impact opportunities for artificial intelligence and translate them into scalable solutions. You will own the AI product roadmap, prioritize investments, and ensure successful execution from concept through deployment and adoption.
As a Product AI Manager, you will:
- Define and execute the strategic vision for Johnson Controls' AI product portfolio.
- Identify and prioritize AI opportunities that support business objectives and customer outcomes.
- Develop and manage AI product roadmaps aligned with enterprise priorities.
- Lead the end-to-end product lifecycle from ideation through deployment and continuous improvement.
- Partner with business stakeholders to translate challenges and opportunities into AI-powered solutions.
- Collaborate with software engineers, data scientists, and analytics teams to deliver scalable products.
- Evaluate emerging AI technologies and recommend innovative solutions that create business value.
- Develop business cases, investment recommendations, and success metrics for AI initiatives.
- Drive adoption and measurable business outcomes through effective stakeholder engagement and change management.
- Lead cross-functional teams while fostering a culture of innovation, accountability, and continuous learning.
How You Will Do It
AI Product Strategy & Roadmap Management
- Define the vision, strategy, and multi-year roadmap for AI products and capabilities.
- Establish portfolio priorities based on business value, customer needs, feasibility, and return on investment.
- Develop business cases outlining expected benefits, risks, resource requirements, and implementation approaches.
- Evaluate industry trends, competitive intelligence, and emerging technologies to inform strategic decisions.
- Create measurable KPIs and success metrics to assess product performance and business impact.
Product Delivery & Execution
- Lead AI product initiatives from concept through launch and post-deployment optimization.
- Translate business requirements into product features, user stories, and prioritized backlogs.
- Partner with engineering and data science teams to ensure successful delivery against roadmap commitments.
- Manage project milestones, timelines, risks, dependencies, and stakeholder communications.
- Monitor product adoption and performance to identify enhancement opportunities and drive continuous improvement.
Business Partnership & Stakeholder Engagement
- Collaborate with business leaders to identify high-impact use cases and strategic opportunities for AI.
- Facilitate discovery workshops and stakeholder sessions to gather requirements and validate opportunities.
- Serve as a trusted advisor to business and technology leaders on AI strategy and investment decisions.
- Build alignment across executive leadership, product teams, and technical stakeholders.
- Present recommendations, roadmaps, business cases, and performance updates to senior leadership.
AI Solution Leadership
- Partner with technical teams to evaluate AI platforms, models, tools, and solution architectures.
- Provide guidance on technology selection, implementation approaches, scalability, and governance.
- Support build-versus-buy evaluations and vendor selection processes.
- Promote responsible AI principles, security standards, and model governance practices.
- Ensure AI initiatives align with enterprise technology strategies and business objectives.
Leadership & Collaboration
- Lead and influence cross-functional teams including product managers, engineers, data scientists, and business stakeholders.
- Foster collaboration across functions to ensure successful delivery and adoption of AI solutions.
- Mentor team members and promote best practices in product management, agile methodologies, and AI innovation.
- Drive accountability for key deliverables, milestones, and business outcomes.
- Champion a culture of innovation, continuous improvement, and customer-focused problem solving.
Qualifications & Experience
- Bachelor's degree in Computer Science, Engineering, Information Technology, Data Science, Business, or a related field, or equivalent practical experience.
- 8+ years of experience in product management, software development, technology strategy, digital transformation, or related disciplines.
- Experience defining product strategies and translating them into successful business outcomes.
- Experience leading enterprise-scale initiatives involving both business and technical stakeholders.
- Demonstrated ability to drive product vision, prioritization, and execution within a complex organization.
- Experience working in Agile product development environments.
AI & Technical Expertise
- Knowledge of artificial intelligence, machine learning, generative AI, Large Language Models (LLMs), AI agents, and emerging AI technologies.
- Experience evaluating and implementing AI-enabled solutions to solve business challenges.
- Familiarity with cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform.
- Understanding of enterprise data ecosystems, analytics platforms, and modern software development practices.
- Knowledge of AI governance, responsible AI principles, and model lifecycle management.
- Experience partnering with engineering and data science teams to deliver production-ready solutions.
Preferred Qualifications
- Master's degree in Computer Science, Data Science, Engineering, Business Administration, or a related field.
- Experience managing AI, analytics, digital, or software product portfolios.
- Experience working with generative AI technologies, AI agents, agentic workflows, or enterprise AI platforms.
- Experience building business cases and measuring return on investment for technology initiatives.
- Knowledge of enterprise platforms, data infrastructure, and cloud-native architectures.
- Experience operating within large, global organizations undergoing digital transformation.
Soft Skills
- Strong communication and executive presentation skills.
- Excellent analytical, strategic thinking, and problem-solving capabilities.
- Ability to influence stakeholders across all levels of the organization.
- Strong collaboration and relationship-building skills.
- Ability to navigate ambiguity and drive results in a rapidly evolving technology landscape.
- Passion for innovation and leveraging emerging technologies to create business value.
Additional Information
Work Location & Arrangement: Glendale, WI or Dallas, TX (Hybrid: 3 days in office)
Travel: Up to 15% domestic travel may be required.
Sponsorship: Johnson Controls will not sponsor applicants for work visas or provide immigration-related employment sponsorship for this position, now or in the future.
Relocation: No relocation assistance
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Johnson Controls, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Johnson Controls AI Hiring
Johnson Controls has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Milwaukee, WI, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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