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AI Expertise: Proven experience in developing and deploying AI models, including familiarity with machine learning algorithms, natural language processing, and data analysis techniques.
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Advanced knowledge of cloud computing technologies such as: Apache Spark, Azure Data Factory, Azure DevOps, Azure Client (Machine Learning), Hadoop, Microsoft Azure, Databricks, AWS, Google Cloud.
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Education: Bachelor or Master' Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or equivalent experience in lieu of degree. Deliver simple solutions to complex problems as a Machine Learning Engineer at GDIT. Here, you'll tailor cutting-edge solutions to the unique requirements of our clients.
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Experienced in managing research or engineering teams in Machine Learning, Natural Language Processing, or related fields. Technical leadership in an applied science team in the area of Large Language Models, Natural Language Processing, Machine Learning and Deep Learning.
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The ML Research Scientist will also have a hands-on role and is expected to customize and create various machine learning algorithms to operate over multi-domain data and optimizing the performance of those algorithms on the data.
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The candidate should have subject matter expertise in designing and implementing machine learning pipelines including a deep understanding of core ML frameworks such as PyTorch, TensorFlow or Scikit-learn and 3rd party ML platforms from Palantir, Dataiku, DataRobot, Alteryx and other vendors.
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Abile Group has an exciting and challenging opportunity for a Machine Learning Engineer on a 10 year contract providing User Facing and Data Center Services supporting an Intelligence Community customer.
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It leverages technologies like robotic process automation (RPA), machine learning, natural language processing, and blockchain to help its clients enhance operational efficiency, improve customer experience, and drive business growth.
$75 an hourFull-timeExpandApply NowActive JobUpdated 19 days ago - UpvoteDownvoteShare Job
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As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.
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Expertise in data strategy, data architecture, data engineering, advanced analytics, data science, data mining, and machine learning. Has experience managing on-shore and off-shore teams and knows how to optimize operating models to ensure data entry, data operations, and data thought leadership is being performed by the right talent base with scale and speed.
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Responsibilities ResponsibilitiesYou will design, write, and maintain semi-automated routines and related documentation and reports for data analysis, data engineering, data management, and mass processing, primarily in the context of hydrologic & hydraulic (H&H) flood modeling and related risk analysis of natural hazards.
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At Cognizant Digital Engineering, a small cross functional team comprised of a Product Manager, an Architect, Full-Stack Developers, UI/UX designers and Big Data analysts builds higher quality software faster siloed individuals working independently.
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Much of our work contributes to innovative research in the fields of sensor science, signal processing, data fusion, artificial intelligence (AI), machine learning (ML), and augmented reality (AR.
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15 years of relevant experience, ideally in data science/analytics, programming, software engineering, control, audit, or risk roles and/or public accounting, with an emphasis in machine learning.
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The Opportunity As a Computer Scientist/Engineer on the Fraud Team, you will be at the forefront of developing and executing an innovative strategy utilizing Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Large Language Models (LLM), and Multi-Agent Systems to combat a variety of fraudulent activities.
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