DescriptionAs 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.
ResponsibilitiesKey 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.
QualificationsDue 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 PyTorch HuggingFace 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 Honeywells 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:
- Bachelors 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/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.
Required Experience:
IC
DescriptionAs 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 ...
DescriptionAs 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.
ResponsibilitiesKey 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.
QualificationsDue 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 PyTorch HuggingFace 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 Honeywells 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:
- Bachelors 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/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.
Required Experience:
IC
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