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About This Role
About Carrier
Carrier Global Corporation, global leader in intelligent climate and energy solutions, is committed to creating innovations that bring comfort, safety and sustainability to life. Through cutting-edge advancements in climate solutions such as temperature control, air quality and transportation, we improve lives, empower critical industries and ensure safe transport of food, lifesaving medicines and more. Since inventing modern air conditioning in 1902, we lead with purpose: enhancing the lives we live and the world we share. We continue to lead because of our world-class, inclusive workforce that puts the customer at the center of everything we do. For more information, visit corporate.carrier.com or follow on Carrier social media at @Carrier.
Summary
Carrier is seeking AI Technical Leader, a principal engineer role with technical leadership in artificial intelligence, machine learning and data analytics combined with engineering domain expertise, to develop & deploy AI solutions for business units and center of excellence teams.
This is a full-time, onsite position operating on a standard Monday through Friday, day shift schedule.
This role can be based in-person at either of the following primary Carrier facilities:
- Indianapolis, IN: 7310 West Morris Street, Indianapolis, IN 46231
- Kennesaw, GA: 1025 Cobb Place Blvd., Kennesaw, GA 30144
Role Responsibilities
- Lead the design, development, deployment and governance of AI solutions for business units and center of excellence teams
- Lead the AI technical solution design for applications including aftermarket, AI integrated products and AI powered engineering processes
- Define and execute the AI strategy, roadmap, standards, and best practices across Carrier Engineering.
- Collaborate with software, systems, electrical, mechanical, and business unit engineering teams to identify and deliver high-value AI solutions.
- Establish governance, security, evaluation, and responsible AI practices for enterprise AI deployments.
- Lead proof-of-concepts, pilots/field-trials, and production deployments while ensuring measurable business impact and operational excellence.
- Guide, mentor engineers and provide technical leadership in AI solution development, model evaluation, and deployment.
- Evaluate emerging AI technologies and drive adoption of innovative solutions across the engineering organization.
Required Qualifications
- Master's degree with 10+ years of industrial experience in Artificial Intelligence, Machine Learning, Data Analytics, Software Engineering or technical domains, -or- Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, Data Science with 8+ years of industrial experience in Artificial Intelligence, Machine Learning, Data Analytics, Software Engineering or technical domains.
- 5+ years of AI technical leadership work experience and engineering domain knowledge.
- Willing and able to travel up to 10% annually, including both domestic and international trips.
Preferred Qualifications
- Strong experience leading teams working on AI, Machine Learning, Data Analytics, or Agentic AI technologies.
- Strong background in Artificial Intelligence, Machine Learning, Data Analytics, Data Engineering and software engineering.
- Hands-on experience in delivering aftermarket fault detection and diagnostics, time-series forecasting and predictive insights/solutions
- Hands-on experience designing, developing, and deploying AI agents and agentic workflows to automate and accelerate engineering processes across the product development lifecycle.
- Strong knowledge of agent orchestration, prompt engineering, planning, reasoning, memory management, tool calling, and Model Context Protocol (MCP) integrations.
- Experience developing AI assistants and knowledge-based AI solutions using enterprise knowledge repositories, vector databases, embeddings, semantic search, and Retrieval-Augmented Generation (RAG) technologies.
- Experience deploying enterprise AI solutions on AWS and/or Azure and integrating them with engineering tools, repositories, APIs, knowledge sources, and enterprise platforms.
- Knowledge of AI evaluation, governance, and monitoring best practices, including Machine Learning Operations (MLOps), Large Language Model Operations and Continuous Integration/Continuous Deployment (CI/CD) pipelines for enterprise AI deployment.
- Experience adapting, fine-tuning, and evaluating Large Language Models (LLMs) using enterprise and domain-specific data.
- Experience in engineering product development, systems engineering, software engineering, electrical engineering, mechanical engineering, controls, simulation, or digital engineering.
- Application experience in HVAC and refrigeration products.
Pay Range
The annual salary for this position is between $117,500.00 - $234,500.00 annually. Factors which may affect pay within this range include, but are not limited to, skills, education, experience, and other unique qualifications of the successful candidate. The pay range will not be below the minimum wage applicable to the jurisdiction in which the applicant is to be employed.Other Compensation
This position is entitled to short-term cash incentives, subject to plan requirements.Benefits
Employees are eligible for benefits, including:
- Health Care Benefits: Medical, Dental, Vision; Wellness incentives
- Retirement Benefits
- Time off and Leave: Paid vacation days, up to 15 days; paid sick days, up to 5 days; paid personal leave, up to 5 days; paid holidays, up to 13 days; birth and adoption leave; parental leave; family and medical leave; bereavement leave; jury duty leave; military leave; purchased vacation
- Disability: Short-term and long-term disability
- Life Insurance and Accidental Death and Dismemberment
- Tax-Advantaged Accounts: Health Savings Account; Health Care Spending Account; Dependent Care Spending Account
- Tuition Assistance
To learn more about our benefits offering, please click here Work with us | Carrier Corporate. The specific benefits available to any employee may vary depending on state and local laws and eligibility factors, such as date of hire and the applicability of collective bargaining agreements.
Carrier EEO Statement and Accommodations Process
Carrier is 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, age, disability or veteran status or any other applicable state or federal protected class. Carrier provides affirmative action in employment for qualified individuals with a Disability and Protected Veterans in compliance with section 503 of Rehabilitation Act and the Vietnam Era Veterans' Readjustment Assistance Act.
If you require a reasonable accommodation to complete the application process, participate in an interview, or otherwise engage in the hiring process, please contact us at [email protected]. We will make every effort to meet your needs in accordance with applicable laws.
Application Deadline
Applications will be accepted for at least 3 days from Job Posting Date: 12 October 2026Job Applicant's Privacy Notice
Please click on the link to review the Job Applicant Privacy Notice.
Use of AI
Technology-enabled tools may support parts of the recruitment process, with oversight by people.
Salary Context
This $117K-$234K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Carrier, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($176K) sits 17% below the category median. Disclosed range: $117K to $234K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Carrier AI Hiring
Carrier has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Indianapolis, IN, US, FL, US. Compensation range: $234K - $234K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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