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Our machine learning infrastructure runs in AWS, with a few deployments spanning into GCP. We train and deploy various state of the art models in a variety of machine learning frameworks.
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We’re looking for a Machine Learning Engineer who will build out and design machine learning infrastructure for a team of data & remote sensing scientists, working collaboratively with the Analytics and Software teams to deliver data-driven products and solutions.
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FinTech, CTO's, CEO's, VC, Artificial Intelligence, Machine Learning, NLP, DevOps, Blockchain, Staffing, Recruitment, Consulting, CIO's, Java, Golang, Ruby, Scala, Hadoop, Docker, Kubernetes, AWS, Cloud, CFO's, financial technology, and nodejs.
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The Data Engineering team in Uplift is building up the next-generation data lake on Databricks to support all data use cases, ranging from business intelligence to machine learning operations.
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Boston, MA, Brooklyn, NY, San Francisco, CA, Washington, D.C., Remote, London, England 11-10-2023 HealthTech Azure Elasticsearch Docker AWS Python Machine Learning. 10+ years of professional experience as a data scientist or machine learning engineer with proven track record of delivering functional product oriented ML solutions.
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Cloud/DevOps engineer to support scripting and automation development in the AWS environment. Cloud/DevOps engineer to support scripting and automation development in the AWS environment.
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Extensive experience with Python Machine Learning toolset (Tensorflow, PyTorch, Scikit-learn, Numpy, Pandas) Evaluate and recommend new tools, technologies, and best practices for machine learning development and deployment.
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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 design.
$174,000 - $218,000 a yearFull-timeExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
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Get notified about new Machine Learning Engineer jobs in. MLOps Engineer ( Machine Learning Operations Engineer) MLOps Engineer (Machine Learning Operations Engineer.
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This role requires a strong understanding of machine learning algorithms, data analysis, and fraud detection techniques. Develop, implement, and maintain machine learning models for fraud detection.
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You will deploy and deliver technical solutions at the intersection of computational chemistry and machine learning, supporting research directions in molecular design across broader gRED and Roche.
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Consulting, Staffing & Staff Augmentation, Managed Services, design thinking, big data, employee engagement, executive hires, digital engineering services, talent development, R&D Labs, Cloud Computing, DataOps, DevOps, RPA, digital transformation, and AIOps.
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Programs on 5G, quantum computing, artificial intelligence, deep learning, quantum computing and from artificial intelligence and deep learning to cyber. programs on 5G, quantum computing, artificial intelligence, deep learning.
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Our current backend stack is Python, Rust, TensorRT-LLM, Kubernetes, AWS. You will work on designing and implementing machine learning models that drive our recommendation systems, retrieval algorithms, and classifiers.
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Full Time) DevOps Engineer at Klarity (United States) | BEAMSTART Jobs. We are looking to add a seasoned DevOps Engineer to our team. Klarity Tech Stack:Frontend: React, TypeScriptBackend: Python, FlaskArchitecture: Microservices: REST and GraphQLDatastore: MongoDB, S3, Redis, RabbitMQCI/CD: Jenkins and Azure DevOpsDeployment: AWS ECS Fargate, EC2.
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machine learning aws devops design jobs Title: devops engineer in San Francisco, CA
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In today's competitive job market, writing quality job ads is critical for attracting top talent to your organization. While networking and candidate referrals are prime real estate for finding qualified candidates, nothing beats the tried-and-true method of writing an extraordinary job ad. But while writing a great job ad is the first step, what's more important is increasing visibility. You could have the most detailed, well-written ad on the internet, but if no one sees it, then you are wasting time (and potentially money!). Employers often believe that job boards are the root of the problem, but you can learn how to increase job ad exposure by tweaking a few steps of your recruitment process.