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Collaborate with data scientists and engineers to design, build, and maintain Azure-based MLOps pipelines for automating machine learning model deployment, monitoring, and maintenance. Implement Azure-based deployment pipelines for deploying machine learning models into production environments.
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Role: Machine Learning Engineer. Optimize machine learning algorithms and infrastructure for performance, scalability, and cost-efficiency. Deploy machine learning models into production environments, ensuring scalability, reliability, and real-time performance.
$70 - $80 an hourExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
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Clean, preprocess, and transform raw data into a suitable format for machine learning models. 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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Reporting to the Director of Machine Learning Engineering the role requires a skilled Machine Learning Engineer to design, build, and deploy machine learning models that will help us derive actionable insights and solve complex problems.
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O9 Solutions is looking for an experienced, talented, and motivated Lead Data Scientist to come join our global team in NA or EMEA. As part of this Data Science team, you will be involved in the end-to-end delivery of our supply chain planning products and building advanced forecasting models with Predictive Analytics, Machine Learning and AI with algorithms such as R, and Python.
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The Reservoir Engineer IV - Machine Learning will perform and lead reservoir engineering technical work for the Permian Business unit working on projects including quantifying reserves and resources and providing reservoir engineering support for efficient development of Civitas’s Permian Assets.
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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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Experience in core data science and predictive analytics methods: Statistics (t-tests, Poisson process), Segmentation and clustering techniques, predictive modeling: e.g., regression, classification, Time Series analysis: e.g., ARIMA, Traditional machine learning methods: e.g., Random Forest, ensemble model techniques, Optimization: e.g. linear programming.
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Assist in the design, development, and implementation of machine learning algorithms and models to solve specific business problems or improve existing processes. Work in a collaborative environment with global teams to drive client engagements in a broad range of industries to design and build scalable AI and Machine Learning solutions, solve business problems, and create value by leveraging client data.
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Requires three (3) years of experience in: (1) data analysis with Python and Python machine learning packages; (2) creating analytics tools to visualize KPIs in relation to equipment and process engineering; (3) MS Excel Macros and Pivot tables; (4) SQL, Tableau, and Spotfire; (5) knowledge of semiconductor process.
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Research, design, build, deploy, and validate new and existing machine learning models and predictive analytics for advanced forecasting in supply chain demand planning that will drive our customers growth from insights that are allow for better decision making.
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Proven experience with enterprise-scale technical delivery experience with AI Services including OpenAI, Schematic Kernel, AWS Bedrock, Machine Learning (or equivalent), Generative AI, LLM customization, NLP, Search, MLOps, Open-source AI frameworks, AI Infrastructure, architecture designAbility to align AI/ML initiatives with broader business goals and outcomes, understanding how AI can drive value and competitive advantage.
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About Blue Yonder: Blue Yonder is a global leader in digital supply chain and omni-channel commerce fulfillment, leveraging advanced technologies such as AI and machine learning to predict, adapt, and fulfill consumer demand efficiently.
$180,722 - $275,639 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Our solutions span use cases across the product life cycle, from product development to mass production and aftermarket operations, and they address an array of industries, including automotive, aerospace, healthcare, consumer products, heavy industry, machine design and research and development.
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As a Senior ML Ops Engineer at SiriusXM, you will be a key player in our Data Platform Team. Your role will be pivotal in deploying, managing, and optimizing machine learning (ML) models, leveraging advanced tools like Databricks and MLFlow.
$135,000 - $209,500 a yearFull-timeExpandApply NowActive JobUpdated 26 days ago
machine learning product manager mountain jobs Company: Google in Dallas, TX
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