Lead AI Engineer

Honeywell is building advanced AI solutions and needs a Lead AI Engineer to provide technical direction while partnering with engineers across the Data Science and Data Engineering organizations. In this role, you will contribute to the design, development, and implementation of AI systems, with a focus on scalable machine learning pipelines and reliable LLM-based capabilities for IT/OT product use cases.

This position is based in Atlanta, GA on a hybrid schedule. For the first 90 days, new hires must be prepared to work 100% onsite Monday through Friday.

Key Responsibilities

• Collaborate with colleagues across multiple teams (Data Science and Data Engineering) to address machine learning system challenges at scale.
• Use distributed training systems to build scalable machine learning pipelines for model training and deployments in the IT/OT Products space.
• Design and implement approaches that improve distributed training execution, including model hyperparameter optimization, training and inference latency, and system-level bottlenecks.
• Research and implement state-of-the-art LLM models for different business use cases, including fine-tuning and serving.
• Maintain ML model performance, uptime, and scale through strong code quality practices, thoughtful system design, 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.

Minimum Qualifications

• Bachelor’s degree from an accredited institution in a technical discipline such as the sciences, technology, engineering, or mathematics.
• 6+ years of industry experience writing production-level, scalable code (for example, in Python).
• 5+ years of experience in one or more machine learning areas such as classification, clustering, optimization, recommendation systems, or deep learning.
• 5+ years of industry experience with distributed computing frameworks such as Spark and the Kubernetes ecosystem.
• 5+ years of industry experience with popular ML frameworks and libraries including Spark MLlib, Keras, Tensorflow, PyTorch, HuggingFace Transformers, and libraries such as scikit-learn, spacy, and genism.
• 5+ years of industry experience with major cloud computing services such as Azure or GCP.
• 1+ years of experience building and scaling Generative AI applications using frameworks such as LangChain, PGVector, Pinecone, AzureML, and VertexAI.
• Experience building agentic AI applications.
• 2+ years of technical leadership leading junior engineers in a product development setting.

Technologies

• Python, PySpark, Spark, Kubernetes
• Spark MLlib, Keras, Tensorflow, PyTorch
• HuggingFace Transformers, scikit-learn, spacy, genism
• Azure, GCP
• Langchain, PGVector, Pinecone, AzureML, VertexAI (including Azure ML)
• CI/CD, Docker, containerization
• LangChain

Benefits

• Employer-subsidized Medical, Dental, Vision, and Life Insurance
• Short-Term and Long-Term Disability
• 401(k) match
• Flexible Spending Accounts
• Health Savings Accounts
• EAP
• Educational Assistance
• Parental Leave
• Paid Time Off for vacation, personal business, sick time, and parental leave
• 12 paid holidays

Notes on Evaluation and Fit

• Honeywell values effective communication, including the ability to explain technical concepts to non-technical audiences and represent Honeywell’s machine learning engineering externally.
• MS or Ph.D. in Computer Science, Software Engineering, Electrical Engineering, or a related field is listed as valued.
• Additional valued factors include containerization proficiency, experience deploying with Azure ML or VertexAI, experience working with CI/CD, and motivation to make downstream modelers’ work smoother.
• Experience building data products with a track record of innovation is described as a plus.
• Minimum experience listed for this role includes 1+ years and an education requirement of a bachelor’s degree in a technical discipline.