Lead AI Engineer

As a Lead AI Engineer here at Honeywell, you will provide technical guidance and support to a team of AI engineers to successfully execute projects. You will be actively involved in the design, development, and implementation of AI solutions, collaborating with related technology groups and working closely with leadership to ensure project requirements align with overarching business objectives.

You will report directly to our Director of Data Science and you'll work out of our Atlanta, GA location on a Hybrid work schedule.

Hybrid Work Schedule Note: For the first 90 days, New Hires must be prepared to work 100% onsite M-F.

In this role, you will impact the development and implementation of cutting-edge AI solutions, driving innovation and technical excellence within Honeywell''s AI initiatives.

Honeywell Technologies is a global, pure-play automation company with a legacy of innovating to help solve the world's most mission-critical challenges, enhancing the quality of life for people and communities around the world. We serve the building, industrial and process sectors with a broad portfolio of services, solutions and products, underpinned by our Honeywell Technologies Accelerator operating system and Honeywell Technologies Forge intelligence layer. By combining the deep domain expertise of our more than 50,000 employees with decades of data from our global installed base, we are uniquely positioned to lead the industrial sector's transition from automation to autonomy.

YOU MUST HAVE

• Bachelor's degree from an accredited institution in a technical discipline such as the sciences, technology, engineering, or mathematics
• 6+ years of industry experience in writing production level, scalable code (e.g. in Python)
• 5+ years of experience with one or more of the following machine learning topics: classification, clustering, optimization, recommendation system, deep learning.
• 5+ years of industry experience with distributed computing frameworks such as Spark, Kubernetes ecosystem, etc.
• 5+ years of industry experience with popular ml frameworks such as Spark MLlib, Keras, Tensorflow, PyTorch, HuggingFace Transformers and libraries (like scikit-learn, spacy, genism etc.).
• 5+ years of industry experience with major cloud computing services like Azure or GCP
• 1+ years of experience in building and scaling Generative AI Applications, specifically around frameworks like Langchain, PGVector, Pinecone, AzureML, VertexAI
• Experience in building Agentic AI applications.
• 2+ years of technical leadership leading junior engineers in a product development setting

WE VALUE

• An effective communicator - you shall be an ambassador of Honeywell's Machine Learning engineering at external forums and can explain technical concepts to a non-technical audience.
• MS or Ph.D. in Computer Science, Software Engineering, Electrical Engineering, or related fields.
• Proficient Python/PySpark coding experience
• Proficient in containerization services
• Proficient in Azure ML or VertexAI to deploy the models
• Experience with working in CICD framework
• Motivation to make downstream modelers' work smoother
• Prior experience in building data products and established a track record of innovation would be a big plus.

BENEFITS OF WORKING FOR HONEYWELL

In addition to a competitive salary, leading-edge work, and developing solutions side-by-side with dedicated experts in their fields, Honeywell employees are eligible for a comprehensive benefits package. This package includes employer subsidized Medical, Dental, Vision, and Life Insurance; Short-Term and Long-Term Disability; 401(k) match, Flexible Spending Accounts, Health Savings Accounts, EAP, and Educational Assistance; Parental Leave, Paid Time Off (for vacation, personal business, sick time, and parental leave), and 12 Paid Holidays. Learn more (https://benefits.honeywell.com/)

The application period for the job is estimated to be 40 days from the job posting date; however, this may be shortened or extended depending on business needs and the availability of qualified candidates. Job Posting Date: October 8, 2026.

ABOUT HONEYWELL TECHNOLOGIES

Honeywell Technologies is a global, pure-play automation company with a legacy of innovating to help solve the world's most mission-critical challenges, enhancing the quality of life for people and communities around the world. We serve the building, industrial, and process sectors with a broad portfolio of services, solutions, and products, underpinned by our Honeywell Technologies Accelerator operating system and Honeywell Technologies Forge intelligence layer. By combining the deep domain expertise of our more than 50,000 employees with decades of data from our global installed base, we are uniquely positioned to lead the industrial sector's transition from automation to autonomy. Learn more (https://www.honeywell.com/us/en)

THE BUSINESS UNIT

The Corporate Strategic Business Group (CORP SBG) at Honeywell is a division focused on corporate-level functions and initiatives that support the overall operations and strategy of the company. It is responsible for overseeing areas such as finance, legal, human resources, communications, and corporate governance, working closely with other business units and SBGs to ensure alignment and coordination across the organization. The CORP SBG plays a crucial role in the overall strategic direction and management of Honeywell''s corporate functions and operations, supporting the company''s business objectives.

Honeywell is an equal opportunity employer. Qualified applicants will be considered without regard to age, race, creed, color, national origin, ancestry, marital status, affectional or sexual orientation, gender identity or expression, disability, nationality, sex, religion, or veteran status. Learn more (https://www.honeywell.com/us/en/company/inclusion-and-engagement)

KEY RESPONSIBILITIES

• Collaborate with colleagues across multiple teams (Data Science and Data Engineering) on unique machine learning system challenges at scale.
• Leverage distributed training systems to build scalable machine learning pipelines for model training and deployments in IT/OT Products space.
• Design and implement solutions to optimize distributed training execution in terms of model hyperparameter optimization, model training/inference latency and system-level bottlenecks.
• Research and impalement state of the art LLM models for different business use cases including finetuning and serving the LLMs.
• Ensure ML Model performance, uptime, and scale, maintaining high standards of code quality and thoughtful design quality and monitoring.
• Optimize integration between popular machine learning libraries and cloud ML and data processing frameworks.
• Build Deep Learning models and algorithms with optimal parallelism and performance on CPUs/GPUs.

KEY RESPONSIBILITIES

• Collaborate with colleagues across multiple teams (Data Science and Data Engineering) on unique machine learning system challenges at scale.
• Leverage distributed training systems to build scalable machine learning pipelines for model training and deployments in IT/OT Products space.
• Design and implement solutions to optimize distributed training execution in terms of model hyperparameter optimization, model training/inference latency and system-level bottlenecks.
• Research and impalement state of the art LLM models for different business use cases including finetuning and serving the LLMs.
• Ensure ML Model performance, uptime, and scale, maintaining high standards of code quality and thoughtful design quality and monitoring.
• Optimize integration between popular machine learning libraries and cloud ML and data processing frameworks.
• Build Deep Learning models and algorithms with optimal parallelism and performance on CPUs/GPUs.