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Experience with cloud computing platforms such as AWS, Azure, or Google Cloud. Data Science/AI frameworks such as TensorFlow, PyTorch, or Keras. Experience with cloud computing platforms such as AWS, Azure, or Google Cloud.
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Apache Spark (Scala and PySpark), Azure Databricks, SQL, Python, Data Visualization, Knowledge in exploratory data analysis skillset will be a plus. Log Analytics, terraform, APIM, Maven, TensorFlow, Jupyter and other Python data tools.
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3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow. Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform.
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NET Core, Java, SQL Server, Docker, Kubernetes, AWS, Azure. Develop with a variety of platforms and technologies, such as PyTorch, Tensorflow, Spark,. NET Core, Java, SQL Server, Docker, Kubernetes, AWS, Azure.
$123,900 - $227,200 a yearFull-timeExpandApply NowActive JobUpdated 3 days ago - UpvoteDownvoteShare Job
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Proficiency in using the native services of cloud providers, including Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). Experience using model lifecycle management tools like MLflow, Neptune, or Amazon SageMaker; serving tools like Seldon Core or TensorFlow Serving; and monitoring and observability tools such as Arize, WhyLabs, or Fiddler.
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As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning (ML) applications and systems at scale. Lead Machine Learning Engineer, Finance Tech.
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Design, build, and maintain our data architecture using technologies like Snowflake, Azure, and Databricks. Direct expertise in building and deploying machine learning models for predictive analytics using libraries such as TensorFlow, PyTorch, or scikit-learn.
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Development experience with PyTorch, Tensorflow, JAX, or similar. Experience training AI/ML models with AWS, Microsoft Azure, or Google Cloud Platform. The AI2 Incubator helps entrepreneurs come up with ideas, find co-founders, engage pilot customers, refine their product, work with research paper authors to incorporate the latest A.I. techniques into their technology, and raise millions of dollars in venture funding.
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Utilizing deep learning tools such as TensorFlow, Keras, or PyTorch to train diverse neural network architectures. Hands-on experience with cloud-based AI/ML services on platforms like Azure, AWS, or GCP.
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AI/ML workloadsPerformance benchmarking Hardware accelerators and specialized devices (GPU, HPU, HPC) Git development workflowKubespray, Kops, or Kubadm Preferred Qualification:Slurm, Volcano, MPI, PyTorch, TensorFlow or other schedulers and AI domain frameworksOn-premises data center networkingCloud development or architecture (AWS, GCP, Azure, etc.
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You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering.
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Proficiency in cloud platforms such as Azure Synapse Analytics, Azure Machine Learning, AWS, and Google Cloud Platform for deploying big data and ML solutions. Expertise in Python and proficiency in ML frameworks (TensorFlow, PyTorch, scikit-learn.
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As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. Lead Machine Learning Engineer (Python, Analytics.
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The Lead Engineer will be an individual technical contributor who has a solid AI/ML foundation, and will help our team accelerate our deliverables. At least 4 years of experience programming with Python, Scala, or Java.
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Experience with cloud computing platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes). Proficiency in machine learning frameworks and libraries such as TensorFlow, PyTorch, or similar.
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