Honeywell invents and commercializes technologies that address some of the world’s most critical challenges around energy, safety, security, air travel, productivity, and global urbanization. We are a leading software-industrial company committed to introducing state-of-the-art technology solutions to improve efficiency, productivity, sustainability, and safety in high-growth businesses in broad-based, attractive industrial end markets.
Requirements
- 7+ 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.
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.
Other
- Bachelor’s degree from an accredited institution in a technical discipline such as the sciences, technology, engineering, or mathematics
- 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.
- 2+ years of technical leadership leading junior engineers in a product development setting
- Due to compliance with U.S. export control laws and regulations, candidate must be a U.S. person, which is defined as, a US 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.
