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Overview Are you a a data expert with broad understanding and experience in solving business problems by integrating statistical analysis, machine learning/artificial intelligence, deep learning, Natural Language Processing (NLP) and Large Language Models (LLMs.
$130,000 - $170,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Design, develop, and implement machine learning models and algorithms to solve complex problems across various domains, including but not limited to recommendation systems, natural language processing, and predictive analytics.
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MS in Computer Science, Information systems, or Computer engineering, Systems Engineering with relevant experience in Text Mining / Natural Language Processing (NLP) tools, Data sciences, Big Data and algorithms.
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Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP) and experience deploying machine learning models using containerization technologies (e.g., Docker, Kubernetes). Join our team as a Machine Learning Engineer, where you will play a pivotal role in leveraging data-driven solutions to drive innovation and business impact.
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Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Generative AI, Large Language Model (LLM), Natural Language Understanding (NLU), Machine Learning (ML), Retrieval-Augmented Generation, Responsible AI, Agent, Evaluation, and Model Adaptation.
$136,000Full-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Must have a strong background in one or more of the following: Mathematical, Statistics, Probability, Deep Learning, Machine Learning, Natural Language Processing, Computer Vision, Recommendation Systems, Pattern Recognition, Large Scale Data Mining or Artificial Intelligence.
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We process billions of mail messages using cutting edge algorithms in areas including but are not limited to: Natural language processing, GenAI, Large Language Models, Machine Learning techniques, big data processing in order of petabytes to: Extract information, build mail content and user knowledge, and interconnect different sources to identify, highlight and amplify what matters.
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About the RoleAs a Machine Learning Engineer - Technical Lead II for Crowe Intelligent Document Analyzer (CIDA), you will design the future of this document extraction product as it transitions from a combination of Natural Language Processing and embeddings models to fully integrate with Crowe’s in-house Generative AI platform and to leverage Large Language Models (LLMs.
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As a Machine Learning Engineer - Technical Lead II for Crowe Intelligent Document Analyzer (CIDA), you will design the future of this document extraction product as it transitions from a combination of Natural Language Processing and embeddings models to fully integrate with Crowe's in-house Generative AI platform and to leverage Large Language Models (LLMs.
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Natural Language Processing (NLP). You enjoy machine learning and understand supporting these solutions in a production environment using tools like Docker and Git; you're familiar with Kubernetes.
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A combination of Machine Learning Engineers and ML DevOps Engineers create and deploy AI functionality to solve complex problems. Partner with Machine Learning DevOps to understand requirements for product scalability, stability, and maintainability, e.g., to improve document queueing or throughput.
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We do this by leveraging state-of-the-art machine learning, modern architectures, and industry experts to create AI-powered software-as-a-service. Lead a team at various competency levels, from junior to senior, providing code review and feedback to support quality deliverables; regularly mentor 2+ Machine Learning Engineers, formally or informally.
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What Additional Experience Makes A Strong Candidate: 4+ years of production experience with Scala 4+ years of production experience with Micro Services / Streaming Services Experience addressing ML problems, particularly in domains such as Time Series, Computer Vision (CV), Natural Language Processing (NLP), and data mining Proficiency with popular ML frameworks (Xgboost, TensorFlow, PyTorch, etc.
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Machine Learning and Deep Learning: Good understanding of: ML algorithms like linear regression, logistic regression, etc., supervised, unsupervised, and reinforcement learning, AI Frameworks like TensorFlow, PyTorch, scikit-learn etc., Neural network, NLP, computer vision, and predictive analytics.
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Use analytic methods and advanced knowledge in machine learning, statistics, text mining, natural language processing, computational semantics, computer vision, and data science to develop creative solutions to complex real-world problems.
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