Agentic AI Engineer Lead

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.

Carrier is seeking an Agentic AI Engineer Lead for the development, deployment, and scaling of Agentic AI technologies across engineering.

This is a full-time, onsite position operating on a standard Monday through Friday, day shift schedule.

• Indianapolis, IN: 7310 West Morris Street, Indianapolis, IN 46231
• Kennesaw, GA: 1025 Cobb Place Blvd., Kennesaw, GA 30144

• →Lead the design, development, and deployment of AI agents and agentic workflows that accelerate engineering processes and productivity.
• →Define and execute the Agentic AI strategy, roadmap, standards, and best practices across Carrier Engineering.
• →Architect AI-powered solutions using Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG), and workflow orchestration frameworks.
• →Collaborate with software, systems, electrical, mechanical, and test 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, and production deployments while ensuring measurable business impact and operational excellence.
• →Mentor engineers and provide technical leadership in AI agent development, model evaluation, and deployment.
• →Evaluate emerging AI technologies and drive adoption of innovative solutions across the engineering organization.

• Master's degree with 7+ years of industrial experience in Artificial Intelligence, Machine Learning, Data Science, Software Engineering or technical domains, -or- Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, Data Science with 5+ years of industrial experience in Artificial Intelligence, Machine Learning, Data Science, Software Engineering or technical domains.
• 3+ years of Agentic AI industry experience.
• Willing and able to travel up to 10% annually, including both domestic and international trips.
• Willing and able to adjust working hours for early morning virtual meetings to support global teams and stakeholders.

• 5+ years of experience leading teams working on AI, Machine Learning, Generative AI, or Agentic AI technologies.
• Strong background in Artificial Intelligence, Machine Learning, Generative AI, and software engineering.
• 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 (LLMOps), 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.

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