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Cloud computing: Google Cloud, Amazon Web Service, Azure, Docker, Kubernetes. Machine Learning Scientist – Quant AI - Vice PresidentThe Machine Learning Center of Excellence invites the successful candidate to apply sophisticated machine learning methods to a wide variety of complex tasks including natural language processing, speech analytics, time series, reinforcement learning and recommendation systems.
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Examples include Azure Machine Learning Studio, Google’s Vertex AI, IBM Watson Studio, Amazon SageMaker and open-source tools like Kubeflow. Examples include MPP data warehouses like Snowflake and Amazon Redshift and all-in-one Apache Spark platforms like Databricks.
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Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) and their machine learning services. Strong understanding of machine learning algorithms, deep learning techniques, and natural language processing.
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Required skills include (3 years exp in): Python or R; Tableau or PowerBI; data analytics; predictive analytics; and (2 years exp in): Machine Learning or Artificial Intelligence software development/software engineering in Azure.
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Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform. Center 3 (19075), United States of America, McLean, VirginiaSenior Lead Machine Learning EngineerAs a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale.
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Experience with cloud computing platforms (, AWS, Azure, GCP) and cloud native computingProficiency in programming languages like Python, Java, Scala, C#, SQLExpertise in big data technologies such as Hadoop, Spark, Kafka, and NoSQL databasesKnowledge of data modeling, ETL processes, and data warehousing conceptsExceptional problem-solving abilities and teamwork skills.
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Responsible for AI model delivery to on-prem infrastructure and cloud platforms (GCP-Vertex AI, Azure ML) · Primary responsibilities also include Vector Database Build on GCP and Azure and building associated data pipeline for Vector Database, Document AI.
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Experience with public cloud platforms like AWS, GCP, and/or Azure. Bloomberg Media is looking for a dedicated and experienced Machine Learning engineer to join the Media Data Science team.
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Previous engineering experience in Data Engineering, LLM's, Gen AI, or cloud platforms like AWS/GCP/Azure. Proven track record in machine learning, with a strong ability to innovate or improve existing ideas, ideally demonstrated through first-author publications or notable projects.
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Experience of work in collaborative development environment (GIT, Azure DevOps, JIRA). Develop and productionalize containerized algos for deployment in hybrid cloud environments (GCP, Azure.
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AWS, Google Cloud, and Microsoft Azure. We are looking for a Senior Software Engineer, ML Backend who is passionate about Machine Learning and AI. AWS, Google Cloud, and Microsoft Azure.
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2+ years of experience working on a cloud environment such as GCP, AWS, Azure, and with dev-ops tooling such as Kubernetes. Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure.
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As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications.
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Tools: Python, Tensorflow, SQL, Spark, Docker, GCP / AZURE / AWS. Machine Learning Engineering - Tech Lead. TEAM: Machine learning and data science is the core part of the company.
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New York City (Hybrid On-Site): $234,700 - $267,900 for Sr. Mgr, Machine Learning Engineering. Senior Manager, Machine Learning Engineering. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms.
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azure job Title: machine in New York, NY
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