Shiftcodeanalytics
Machine Learning Lead Engineer : Fractal AI
Shiftcodeanalytics, Seattle, Washington, us, 98127
Interview: Video
Visa: USC, GC, GC EAD
Locations: Minneapolis, Seattle, Chicago, or Bay Area (candidate must be local to any of these locations)
Description
Mandatory Skills:
3-5 years experience working with MLOps systemMust have Healthcare industry experience3-5 years of experience in developing/working on MLOps systems, and an overall experience of 10+ years in Data ScienceExpert on using Python to develop scripts for MLOpsExpert on cloud platform (Azure/AWS) developer environmentsRequirements:
Designing data pipelines and engineering infrastructure to support enterprise machine learning systems at scaleOrganizing offline models that data scientists build and turning them into a real machine learning production systemDeveloping and deploying scalable tools and services for our clients to handle machine learning training and inferenceUnderstanding application of software engineering rigor and best practices to machine learning, like CI/CD, automation, auditability, versioning, and data security (KubeFlow, MLFlow)Developing and maintaining Client systems built with open source tools (Kale)Facilitating the development and deployment of proof-of-concept machine learning systemsPrevious work with data drift model driftExpertise on Kubernetes, Kafka, and/or DockerExposure to deep learning approaches and modeling frameworks (PyTorch, TensorFlow, Keras, etc.)Expert in using AWS or Azure Client tools (SageMaker/Azure Client, etc.) [preferred]
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Visa: USC, GC, GC EAD
Locations: Minneapolis, Seattle, Chicago, or Bay Area (candidate must be local to any of these locations)
Description
Mandatory Skills:
3-5 years experience working with MLOps systemMust have Healthcare industry experience3-5 years of experience in developing/working on MLOps systems, and an overall experience of 10+ years in Data ScienceExpert on using Python to develop scripts for MLOpsExpert on cloud platform (Azure/AWS) developer environmentsRequirements:
Designing data pipelines and engineering infrastructure to support enterprise machine learning systems at scaleOrganizing offline models that data scientists build and turning them into a real machine learning production systemDeveloping and deploying scalable tools and services for our clients to handle machine learning training and inferenceUnderstanding application of software engineering rigor and best practices to machine learning, like CI/CD, automation, auditability, versioning, and data security (KubeFlow, MLFlow)Developing and maintaining Client systems built with open source tools (Kale)Facilitating the development and deployment of proof-of-concept machine learning systemsPrevious work with data drift model driftExpertise on Kubernetes, Kafka, and/or DockerExposure to deep learning approaches and modeling frameworks (PyTorch, TensorFlow, Keras, etc.)Expert in using AWS or Azure Client tools (SageMaker/Azure Client, etc.) [preferred]
#J-18808-Ljbffr