Interested in this AI/ML Engineer role at Trane Technologies?
Apply Now →Skills & Technologies
About This Role
Be a part of our mission! As a world leader in creating comfortable, sustainable, and efficient climate solutions for buildings, homes and transportation, it's our responsibility to put the planet first. For us at Trane Technologies, and through our businesses including Trane® and Thermo King, sustainability is not just how we do business—it is our business. Do you dare to look at the world's challenges and see impactful possibilities? Do you want to contribute to making a better future? If the answer is yes, we invite you to consider joining us in boldly challenging what's possible for a sustainable world.
Learn about our benefits designed for you to Thrive at work and at home.
We boldly go.
Where is the work:
Monday to Thursday, work onsite with your colleagues. Fridays, choose your work location, balancing what your work requires. What’s in it for you?
Join Trane Technologies as an AI \& Data Delivery Specialist! Our Commercial HVAC Analytics and Technology Solutions team is seeking an AI \& Data Delivery Specialist to serve as the entry point for project delivery to manage the intake of new AI and data project requests, prioritizing the project pipeline, and acting as the dedicated Scrum Master for technical delivery teams.
The AI \& Data Delivery Specialist will lead complex, cross\-functional initiatives by translating business problems into actionable roadmaps, managing budgets, and aligning stakeholders and teams across the organization.
The ideal candidate has experience in technical project management, preferably in data, analytics, or AI\-focused environments, and brings strong expertise in configuring Jira to support effective delivery. This person should also have strong data literacy and practical experience serving as a Scrum Master in an Agile environment.
What you will do:
- Project Intake \& Portfolio Management: Serve as the primary point of contact for new AI and data project requests. Assess incoming projects for business impact, feasibility, and priority. Manage the backlog and continuously reprioritize work as business needs evolve.
- Project Delivery: Lead end\-to\-end delivery of AI, data, and analytics initiatives in partnership with the broader team, IT, other Data \& Analytics teams, and business stakeholders. Define project scope, track progress, manage backlogs, maintain clear communication, and ensure projects are delivered on time and within budget.
- Jira Administration \& Tracking: Own and manage the team’s Jira environment. Build and maintain Kanban and Scrum boards, optimize workflows, configure automation, and use Jira reporting and analytics to monitor capacity and delivery performance.
- Scrum Master Facilitation: Lead scrum ceremonies for delivery teams, including daily stand\-ups, sprint planning, backlog refinement, and retrospectives.
- Resource Management \& Budgeting: Develop resource plans using internal and external resources and identify additional support as needed. Manage vendor contracts, statements of work (SOWs), and purchase orders/requisitions. Partner with Procurement, IT, and FP\&A to track and manage project expenses accurately.
What you will bring:
- Project Management Experience: 3\+ years of experience managing technical projects, with a focus on delivering data products, data pipelines, business intelligence solutions, or AI initiatives.
- Technical Background / Data Literacy: 2\+ years of hands\-on experience in a technical data\-related role (such as Data Analyst, Data Engineer, or Business Analyst), with a strong understanding of data and analytics concepts.
- Jira Expertise: Strong experience with backlog and sprint management, workflow and issue tracking, reporting and metrics, and permissions/integrations within Jira.
- Agile / Scrum Experience: Deep knowledge of Agile Scrum and Kanban methodologies, along with practical experience serving as a Scrum Master for technical teams.
- Education: Bachelor’s degree in a related field or equivalent relevant experience.
Preferred Qualifications
- Experience with data and analytics tools such as Tableau, Google BigQuery, Dataiku, or similar platforms
- Certifications in Scrum, Agile, or Project Management
- Experience working in a large, complex organization
Annual Base Salary Range or Hourly Base Pay Range:
$72,611\.66 \- $116,700\.00Compensation Type:
SalaryIncentive Eligible:
NoSales Commission Eligible:
NoDisclaimer: We strive to provide competitive compensation for this position, tailored to a variety of factors. The actual compensation will depend on elements such as seniority, merit, geographic location, education, experience, travel requirements, and union designation. Our compensation range is generally based on the national average for the country. Additionally, benefits may vary depending on the region, business alignment, union involvement, and employee status.
Thrive at work and at home:
- Benefits kick in on DAY ONE for you and your family, including health insurance and holistic wellness programs that include generous incentives – WE DARE TO CARE!
- Family building benefits include fertility coverage and adoption/surrogacy assistance.
- 401K match up to 6%, plus an additional 2% core contribution \= up to 8% company contribution.
- Paid time off includes 15 vacation days, 9 paid holidays, 3 floating holidays, sick leave, and additional options to support volunteer and parental leave.
- Educational and training opportunities through company programs along with tuition assistance and student debt support.
Disclaimer: Benefit offerings may vary depending on Collective Bargaining Agreements and local/state regulations.
Safety Sensitive Role:
No
The company designates certain roles as Safety Sensitive. Safety Sensitive roles may require that you pass additional drug screening.
We offer competitive compensation and comprehensive benefits and programs. We are an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, pregnancy, age, marital status, disability, status as a protected veteran, or any legally protected status.
Salary Context
This $72K-$116K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Trane Technologies, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($94K) sits 56% below the category median. Disclosed range: $72K to $116K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Trane Technologies AI Hiring
Trane Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Davidson, NC, US. Compensation range: $116K - $116K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.