AI Technical Lead

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  or follow on Carrier social media at mary 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…