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Demonstrated expertise in machine learning, deep learning, and NLP, with a strong portfolio in large language models like GPT-3, BERT, and RAG.Advanced proficiency in Python, with hands-on experience in ML libraries and frameworks such as TensorFlow and PyTorch.
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1 or more professional cloud certifications: AWS Cloud Practitioner, AWS Solution Architect - Associate, AWS Developer - Associate, AWS Security - Specialty, or AWS Solution Architect - Professional.
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Understanding or hands on experience with Lex, Nuance Mix, Dialog Flow, AWS and Google Cloud, R, Power BI, Tableau, Alteryx; Using natural language processing (NLP), machine learning (ML), Generative AI, and other relevant AI technologies and platforms.
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Experience with public cloud providers such as AWS, GCP, or Azure, or cloud-based machine learning platforms. Our projects include machine learning-powered products, cloud computing infrastructure and strategy, open source stewardship, natural language processing, and more.
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We are seeking a highly motivated individual with hands-on experience in model development and implementation using Machine Learning, Deep Learning, Natural Language Processing and/or Generative AI modeling techniques.
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Hands-on experience using IaC software tools (CloudFormation, Terraform, Azure Resource Manager, GCP Cloud Deployment Manager etc) and CI/CD tools (IDEs, GitLab, GitHuB, Jenkins, CircleCI, etc.
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Candidate with equivalent AWS services will be considered (AWS Beanstalk, API Gateway, AWS KMS, S3, VPC, VPC Private Endpoint, IAM Roles) - Coding, deploying, and validating the IaC (Infrastructure as Code) solution through Terraform/Golang - GitHub Actions pipeline.
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Deep knowledge in Machine Learning, Deep Learning, Data Mining, Information Retrieval, Statistics. 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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The Risk Management & Compliance Technology Machine Learning team at JPMorgan Chase focuses on solving challenging business problems such as Anti-Money Laundering and Surveillance through data science and machine learning techniques across Risk, Compliance, Conduct and Operational Risk. Are you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area.
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Daily Tasks: Skills in nutshellDrupal, PHP, Angular, Express with Angular Universal, NodeJS Monitoring (PM2), SOLR, Java, AWS Cloud Engineering, AWS Cloud Services, Route 53, Load Balancers, EC2, Acquia CloudAutoscaling groups, Mem Cache, Secrets Manager, CloudFront, Code Deploy, Code Pipeline, VPC, CloudFormation, IAM,Secuirty Groups, S3, CloudWatch, EFS, WebSphere Portal,Ansible, Apache, Ngnix, NodeJS, Varnish, Jenkins, Terraform, LSyncD, Bash Scripting, Splunk.
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As a Director, Machine Learning Engineer you will partner with Data Scientists, Data Engineers, Data Analysts and other professionals to implement machine learning models that will deliver stability, producibility, scalability and integration with other products and services.
$244,900 a yearFull-timeExpandApply NowActive JobUpdated 2 months ago - UpvoteDownvoteShare Job
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In our Data science track we prepare you to get job as one of the following: Python developer, a data analyst, data visualization developer, a statistician, a machine learning engineer or a data scientist.
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Relevant cloud certifications such as AWS Certified Solutions Architect - Professional, Azure Solutions Architect Expert, or Google Cloud Professional Cloud Architect. Extensive experience with major cloud platforms such as AWS, Azure, or Google Cloud.
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Experience building loan-level credit/prepayment models from data preparation, data analysis, and model estimation through deployment into productionExperience with generalized regression models as well as machine learning frameworksVery strong programming and software design skills (Python, C.
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Experience may be gained concurrently and must include one (1) year in each of the following:-Building statistical models and machine learning models using large datasets from multiple resources-Working with Customer, Content, or Product data modeling and extraction-Using database technologies such as SQL or ETL-Applying specialized modelling software including Python, R, SAS, MATLAB, or Stata.
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