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Experience working with big data distributed programming languages, and ecosystems such as Spark, Hadoop, MapReduce, Pig, Kafka. Clean, preprocess, and transform raw data into a suitable format for machine learning models.
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Relevant work experience designing and implementing big data and machine learning solutions. Serve as a lead technical resource for Generative AI, Big Data/Machine Learning, partnering with internal HBS technical partners and stakeholders, and vendors in development efforts.
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Experience in big data frameworks (e.g., Spark/Hadoop/Flink), experience in resource management and task scheduling for large-scale distributed systems. Responsible for improving the workflow of model training and serving, data pipelines, and resource management for the multi-tenancy machine learning systems.
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Experience in data structures, algorithms, and deep learning libraries, including TensorFlow and Keras; Experience in Python and Java programming languages; Experience with Amazon Web Services; Experience with big data tools, data pipelines, RDBMS, and NoSQL databases; and Experience with real-time bidding (RTB) ecosystem.
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We are a close-knit and ambitious team that specializes in rapid development, big-data geospatial systems. Hands-on experience building data and machine learning pipelines.
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The Machine Learning Engineer will be responsible for architectural design and planning, advanced data pipelines, model integration and optimization, scalability, performance and research and innovation supporting production AI systems.
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Position Summary: We’re looking for a highly technical Senior Engineer, Machine Learning & Computer Vision to help develop our pipeline and models that process hundreds of millions of georeferenced images from around the globe.
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Experience with big data, machine learning, and numerical programming frameworks (e.g., TensorFlow, Python, MATLAB). Experience in data and information management as it relates to big data trends and issues within businesses.
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You liaise with the product marketing management and engineering teams to stay on top of industry trends and devise enhancements to Google Cloud products. Experience building machine learning solutions and leveraging specific machine learning architectures (e.g., deep learning, LSTM, convolutional networks.
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Collaborate with data scientists, software engineers, domain experts, and client stakeholders to understand requirements, gather feedback, and integrate machine learning solutions into larger systems or products.
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We're a company predominantly focused on cyber security for our government customers yet our expertise in other areas include big data analytics, instructional design, information management, and computer network infrastructure.
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Partner directly with product management to prioritize solutions impacting customer adoption to Google Cloud. Provide in-depth machine learning expertise to support the technical relationship with Google’s customers, including product and solution briefings and proof-of-concept work.
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Python with Django and Flask, Spark, Big-Data stack - Hive< Kafka & NoSQL's. Evaluation, selection and implementation of tools and technology for data catalogs, data governance, data quality and data management.
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Implement Machine Learning (ML) and Big Data platforms in Hybrid and multi-cloud environment specifically in AWS SageMaker environment. The client is looking for Machine Learning Engineer to join a diverse team dedicated to providing best in class data services to their customers, stakeholders and partners.
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2+ years of education or professional experience in applying machine learning and data mining techniques to real problems with copious amounts of data. Experience with very large-scale data processing/analysis (a.k.a. big data.
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big data management jobs Title: machine learning engineer
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