As a Lead AI Engineer at Honeywell, you will be at the forefront of driving innovation and leading the development and implementation of cutting-edge AI solutions. You will play a pivotal role in shaping the future of AI initiatives at Honeywell and contribute to the growth and success of the company.

Due to compliance with U.S. export control laws and regulations, candidate must be a U.S. Person, which is defined as, a U.S. citizen, a U.S. permanent resident, or have protected status in the U.S. under asylum or refugee status or have the ability to obtain an export authorization.

You Must Have:

• Minimum 7 years of industry experience in writing production level, scalable code (e.g. in Python)
• Minimum 5 years of experience with one or more of the following machine learning topics: classification, clustering, optimization, recommendation system, deep learning.
• Minimum 5 years of industry experience with distributed computing frameworks such as Spark, Kubernetes ecosystem, etc.
• Minimum 5 years of industry experience with popular ml frameworks such as Spark MLlib, Keras, Tensorflow, Py Torch, Hugging Face Transformers and libraries (like scikit-learn, spacy, genism etc.).
• Minimum 5 years of industry experience with major cloud computing services like Azure or GCP
• Minimum 1 year of experience in building and scaling Generative AI Applications, specifically around frameworks like Langchain, PGVector, Pinecone, AzureML, VertexAI
• Experience in building Agentic AI applications.
• 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.
• Minimum 2 years of technical leadership leading junior engineers in a product development setting

Preferred Qualifications:

• Bachelor’s degree from an accredited institution in a technical discipline such as the sciences, technology, engineering, or mathematics MS or Ph.D. in Computer Science, Software Engineering, Electrical Engineering, or related fields.
• Proficient Python/Py Spark 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.

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.