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We have developed a vertically integrated technology platform in which we leverage additive manufacturing, artificial intelligence, and autonomous robotics to 3D print rockets. Hands-on experience with fabrication/prototyping such as machining, 3D printing, welding, woodworking.
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Practical hands on experience with: NLP (especially operationalization in production models), Advanced machine learning techniques and tools (especially Tensorflow), modern Data Analytics & Visualization tools (Looker preferred.
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Hands-on experience with automating WMS functional and integration testing using HP-UFT Automation tool and Postman. Hands-on experience with automating WMS functional and integration testing using HP-UFT Automation tool and Postman.
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4+ years of hands-on experience in one of the following technical domains: machine learning, recommendation systems, pattern recognition, NLP, artificial intelligence, or a related technical field.
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At IBM, we pride ourselves on being an early adopter of artificial intelligence, quantum computing and blockchain. Required Technical and Professional Expertise Certified MuleSoft Developer, MuleSoft Platform Architect, MuleSoft Integration Architect 4 years of hands-on development experience with MuleSoft Anypoint Platform 10 years as an Integration Architect working on enterprise-scale solutions for industry leaders Strong experience in designing and implementing integration solutions using MuleSoft Anypoint Platform.
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Hands-on coding experience using Typescript/Javascript, Java, Python, C, C. Backed by artificial intelligence and machine learning, Jerry simplifies and automates owning and maintaining a car while providing personalized services for all car owners' needs.
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Extensive hands-on experience leading qualitative and quantitative UX research in a product design environment across the Design Thinking stages. Through research and development initiatives in our FinLabs we develop solutions for modernization, from Blockchain and Artificial Intelligence to Data Science models, Digital Underwriting, mobile-first applications and more.
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Hands on working knowledge and experience around Oracle RDBMS, PostgreSQL, MSSql, Redshift, Databricks. We're driving the company's digital transformation and modernization initiatives and expanding business capabilities using industry best practices and advanced technologies such as cloud, artificial intelligence and robotics process automation.
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You have extensive hands-on experience in building scalable data platforms and reliable data pipelines using technologies such as Spark, ElasticSearch, Databricks, Clickhouse, AWS Kinesis, and/or Kafka.
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Leidos is seeking an Artificial Intelligence (AI) Cybersecurity Researcher / Engineer to support the Department of Homeland Security (DHS) Office of the Chief Information Security Officer.
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Hands on experience in data visualization and developing intuitive dashboards, using tools such as Tableau and Qlik Sense. Understanding of machine learning, artificial intelligence, and cloud services (Merlin, Alteryx, IBM, AWS etc.
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Hands-on experience in two (or more) areas of the data and analytics technical domains - Enterprise cloud data warehousing, integration, preparation, and visualization along with artificial intelligence, machine learning, data science, data modeling, data management, and data governance.
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Hands-on experience with electrical CAD tools (Altium, Solidworks Electrical, Siemens NX, AutoCAD, Creo Schematics, OrCAD, etc.) Hands-on experience with electrical design for various systems (low voltage power, controls, instrumentation, ordnance, radio frequency, etc.
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Experience with the following modeling, simulation, and analysis software such as MATLAB, Open Mission Systems (OMS), real time operating systems (RTOS), hands-on development and troubleshooting on embedded targets, complex digital device hardware including FPGAs and DSPs, hardware description languages, VPX, VITA, and SOSA, GPU, Cuda, OpenGL, RF Technologies and Software Defined Radios,EO/IR Technologies and knowledge of different IP protocols, interfaces and hardware subsystems.
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Sustained (e.g., at least 10 years), hands-on experience with: data modeling and data engineering, feature engineering, model lifecycle management, and cloud infrastructure; supervised and unsupervised deep learning algorithms.
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