Interested in this AI/ML Engineer role at Johnson Controls?
Apply Now →Skills & Technologies
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 really matters. 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/sick time
- Comprehensive benefits package including 401K, medical, dental, and vision care
- On the job/cross training opportunities
- Encouraging and collaborative team environment
- Dedication to safety through our Zero Harm policy
Johnson Controls International (JCI) is seeking an AI Engineer to join our innovative and impact\-driven Data Science and Analytics team. This role is ideal for an engineer who combines solid software, data, and ML engineering skills with hands\-on Generative AI experience—and a data scientist's curiosity for how models behave. You build the pipelines, tooling, and applications that turn AI and LLM models into dependable production software.
As an AI Engineer, you will independently own the end\-to\-end delivery of defined AI projects—from data pipeline through deployed application. You will make sound technical decisions within your scope, partner directly with cross\-functional stakeholders, and guide junior engineers on specific problems as you deliver measurable business value.
How you will do it
Generative AI Systems \& Applications
- Develop and deploy Generative AI systems and LLM\-powered applications (e.g., GPT, Claude, LLaMA) for use cases such as enterprise search, document summarization, and conversational AI.
- Apply prompt engineering, fine\-tuning, and orchestration techniques to adapt foundation models for domain\-specific applications.
- Build agentic workflows and task\-specific AI agents—using Palantir AIP or the Microsoft Agent Framework—that orchestrate tools, retrieval, and reasoning.
- Evaluate and improve model outputs for accuracy, relevance, latency, and cost, applying data science techniques to measure and validate performance.
Data, ML \& Software Engineering
- Build and maintain the data pipelines that feed AI systems—ingestion, transformation, and ETL across structured and unstructured sources (e.g., Snowflake, Azure).
- Develop and operate ML pipelines and MLOps workflows—training, evaluation, deployment, and monitoring—using CI/CD, containerization (Docker), and model serving.
- Build reusable components, services, and APIs around AI models that help the team ship features faster.
- Implement retrieval and embedding workflows (RAG, vector databases) for scalable, accurate knowledge retrieval.
- Apply software engineering best practices—testing, version control, and code review—across your projects.
Business Impact \& Stakeholder Communication
- Partner with cross\-functional stakeholders to translate business challenges into AI solutions.
- Support workshops and proofs\-of\-concept that demonstrate the value of LLM and agent use cases across business units.
- Translate model outputs, data findings, and technical tradeoffs into clear insights for non\-technical audiences.
Mentorship \& Collaboration
- Guide junior engineers on specific technical problems and code quality.
- Contribute to design discussions and technical decisions within the team.
- Share knowledge and help raise the bar on engineering and data science practices.
Qualifications \& Experience
- Education in Computer Science, Software Engineering, Data Engineering, Data Science, or a related technical or quantitative discipline.
- 2–5 years of experience in software, data, ML engineering, or data science, including hands\-on work with LLMs or generative AI.
- Demonstrated success delivering data or ML pipelines and AI/ML solutions to production.
- Experience with data science fundamentals—exploratory analysis, statistical modeling, or classic ML (classification, regression, forecasting).
- Experience with cloud AI platforms such as Azure OpenAI/Azure ML, AWS SageMaker/Bedrock, or Google Cloud Vertex AI.
Technical Expertise
- Strong proficiency in Python and SQL, with good software engineering habits—testing, version control, and clean code.
- Hands\-on experience with the Generative AI stack: prompt engineering, fine\-tuning (e.g., LoRA), LLM orchestration, and agent frameworks (LangChain, Semantic Kernel, Microsoft Agent Framework).
- Experience building ETL and ML pipelines and applying MLOps practices (CI/CD, Docker, model serving).
- Familiarity with data science libraries and workflows—pandas, scikit\-learn, and model evaluation and experimentation.
- Experience with JCI's stack—or comparable platforms—including Palantir AIP, Azure ML, Microsoft Agent Framework, Power Automate, and Snowflake.
- Working knowledge of embeddings, vector databases, and retrieval systems.
Soft Skills
- Ability to own projects and communicate progress, risks, and tradeoffs clearly.
- Strong collaboration skills across product, engineering, and business teams.
- Comfortable presenting technical and analytical work to both technical and non\-technical stakeholders.
- Self\-directed problem solver who manages priorities independently.
Preferred Qualifications
- Experience with IoT, edge analytics, or smart building systems.
- Familiarity with LLMOps, LangChain, Semantic Kernel, or similar orchestration frameworks.
- Data science depth—statistical modeling, experimentation, or deep learning (forecasting, computer vision, or NLP).
- Experience with the Microsoft ecosystem (Microsoft 365 Copilot, SharePoint, Power Platform, Snowflake).
- Knowledge of data privacy and governance considerations for enterprise LLM usage.
Additional Information
Work Location \& Arrangement: Glendale, WI, hybrid
Sponsorship: Johnson Controls will not sponsor applicants for work visas or provide immigration\-related employment sponsorship for this position, now or in the future.
HIRING SALARY RANGE: $85,000 \- $110,000 (Salary to be determined by the education, experience, knowledge, skills, and abilities of the applicant, internal equity, location and alignment with market data.) The posted salary range reflects the target compensation for this role. However, we recognize that exceptional candidates may bring unique skills and experiences that exceed the typical profile. If you believe your background warrants consideration beyond the stated range, we encourage you to apply. To support an efficient and fair hiring process, we may use technology assisted tools, including artificial intelligence (AI), to help identify and evaluate candidates. All hiring decisions are ultimately made by human reviewers. This position includes a competitive benefits package. For details, please visit the About Us tab on the Johnson Controls Careers site at https://jobs.johnsoncontrols.com/about\-us
Salary Context
This $85K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($97K) sits 55% below the category median. Disclosed range: $85K to $110K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
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. Compensation range: $110K - $110K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.