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Manage services from Public Cloud Hyperscalers like Microsoft Azure, Amazon AWS, Oracle OCI, Google GCP, and Alibaba Cloud on services like Cloud Compute, Storage Services, Data Services, DevOps, CI/CD, MicroServices, Service Mesh, Containerization, Artificial Intelligence, Machine Learning platforms and End-to-End Observability.
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Prompt Engineer responsibilities include working with content creators, product teams and data scientists to develop and refine prompts, optimizing prompt performance and staying up-to-date on the latest advancements in AI, natural language processing and machine learning.
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As a part of this client-centric team, which is focused on Cartier employee development throughout the talent lifecycle, you will work cross-functionally with various levels of leadership, as well as a variety of subject matter experts within the organization, to identify learning needs, then implement solutions.
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Possess an advanced degree in geotechnical engineering, have experience in processing foundation reports through Caltrans, and some experience as a project manager, and be a registered civil engineer.
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Security and Compliance - Implement security best practices to safeguard customer data and ensure compliance with relevant industry regulations (such as GDPR, PCI DSS). Technical Implementation - Develop and implement Shopify-based websites, themes, and applications using HTML, CSS, JavaScript, Liquid (Shopify's template language), and other relevant technologies.
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High familiarity with data platform and applications such as S3, Tableau, Databricks, Redshift and Airflow. Experience with data governance tools such as Alation, Collibra, Databricks Unity catalog, Informatica etc.
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Performs basic trial runs to check accuracy of machine settings or programmed control data. Technical Skills - Understands and has working knowledge of the manufacturing production control and use of machinery operation process, procedures and function; use and understand basic computer entry and processing skills.
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Net, C#, Qlik, Power BI, Machine Learning, Azure Data Factory, RedShift, UiPath, Cloud, RPA, AWS, Redshift, Kinesis, QuickSight, SageMaker, S3, Databricks, AWS Lake Formation, Snowflake, Python, Qlik, Athena, Data Pipeline, Glue, Star Schema, Data Modeling, Performance Tuning, SQL Individual salaries that are offered to a candidate are determined after consideration of numerous factors including but not limited to the candidate’s qualifications, experience, skills, and geography.
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Showcasing excellence in refining and optimizing software for machine learning, ensuring it meets high standards of maintainability, quality, resilience, and performance. Familiarity with the graphics pipeline and 3D software packages for manipulating data.
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A deep technical background in applied data science, spanning some or all statistics, experimentation, machine learning, causal methods, optimization techniques, data engineering and architecture (applied to large data), and behavioral analytics.
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The purpose of the position is to collect and analyze a variety of data to meet legislative requirements to publish MPD's administrative information. Expertise in at least one statistical programming language (R, Python, etc.
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Truleo.co) Truleo is an advanced natural language processing (NLP) platform that automates the review and analysis of body worn cameras (BWC). We accomplish this by surfacing risky interactions, identifying trends in officer behavior, and creating a cost effective way for departments to analyze and search through all of their BWC data.
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Advanced degree in a field related to coastal policy, health or water quality (e.g., environmental science; environmental and/or natural resource management, planning or policy; environmental systems analysis, environmental risk analysis, etc.
$30.27 - $33.85 an hourFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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As a Senior Machine Learning Engineer, you will work with the data science team and report to the Head of Engineering to focus on supporting and developing machine learning solutions.
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Continuously improve knowledge and abilities in the areas of software and data engineering, statistical learning, and machine learning. Implement new statistical, or machine learning, models and maintain existing models, used in all facets of player acquisition and game play.
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