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about job Create and maintain code, documentation, testing and deployment frameworks, tools and infrastructure, working closely with engineers and domain experts on AI/ML models and pipelines Machine learning model training pipeline, how to manage the data, how to get data into production. Develop environments for building, testing, tracking production AI models and data across data pipelines used in primary and secondary genomic analysis Be a technical expert to develop AI models within a consistent ML environment, automate training and data/model management
Job Responsibility:
Create and maintain code, documentation, testing and deployment frameworks, tools and infrastructure, working closely with engineers and domain experts on AI/ML models and pipelines
Machine learning model training pipeline, how to manage the data, how to get data into production
Develop environments for building, testing, tracking production AI models and data across data pipelines used in primary and secondary genomic analysis
Be a technical expert to develop AI models within a consistent ML environment, automate training and data/model management
Requirements:
Bachelors in Computer Science or equivalent experience
Min 3 years in machine learning and operations, ideally in a hands-on MLOps role
Experience deploying APIs and packages
Experience with ML ops platforms (MLFlow, W&B
Kubernetes, Docker, dask, rapids.ai, Ray)
Experience with ML frameworks (Tensorflow, keras, Pytorch, xgboost, sklearn)
Strong Python coding skills – experience with unit testing, code reviews, version control
Or could be DevOps Engineer with ML experience. OR Bioinformatics Engineer with AI/ML ops related experience OR ML Engineer with DevOps experience
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