Lead AI Engineer

Honeywell•Atlanta, GA

6d•Hybrid

About The Position

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. 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.

Requirements

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

Nice To Haves

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.

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 implement 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.