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We work as a team: We are a collaborative company comprised of highly-educated industry leaders in radar systems, image and signal processing, machine learning, hardware development, and software engineering.
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Preferred Skills:- Previous experience as a software engineer, applied mathematician, or in roles involving DevOps, MLOps, Databricks, and Apache Spark is highly regarded. machine learning We are actively seeking a Senior Machine Learning Engineer to contribute to innovative projects for a prestigious Global Financial Services organization.
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Including software presentations, demonstrations, and PoCs (as a pre-sales engineer, sales engineer, solution architect, or equivalent) Our technology, powered by the latest natural language processing and deep learning technology, is used across industries, to power digital workplaces, accelerate digital transformation, and provide workers with contextual and secure access to the data they need to achieve their missions.
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Data: Good understanding of data & data processing tools (e.g. Spark, Kafka, SQL), of relational database technologies and of analytics databases (e.g. Redshift, Vertica, Snowflake) Big Data & Streaming: 2+ years using big data and/or streaming technologies (e.g. Apache Spark, Apache Kafka, Apache Flink.
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Full Time) Senior Software Engineer - Streaming at TRM Labs (United States) | BEAMSTART Jobs. Our next-generation platform, which combines threat intelligence with machine learning, enables financial institutions and governments to detect cryptocurrency fraud and financial crime at an unprecedented scale.
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As a Machine Learning Engineer - Technical Lead II for Crowe Intelligent Document Analyzer (CIDA), you will design the future of this document extraction product as it transitions from a combination of Natural Language Processing and embeddings models to fully integrate with Crowe's in-house Generative AI platform and to leverage Large Language Models (LLMs.
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Applying Strong knowledge of EcoSruxure Plant /Machine, Smart Factory Solutions, Industry 4.0, Control & Visualization Software, SCADA, edge-to-enterprise analytics, AI, machine learning, industrial internet of things (IIoT), AR/VR, Digital Twin, Unified Operations Center, APM, APC, OTS Software and Services along with MES and Batch.
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Experience with at least one major Massively Parallel Processing (MPP) or cloud database technology (Snowflake, Redshift, Big Query) In doing so, they contribute to a series of cross-functional services and technologies, including Quality Assurance, Data Analytics, Software Development, Broadcast Infrastructure, and Networking & Security.
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Experience with designing and maintaining data warehouses and/or data lakes with big data technologies such as Spark/Databricks, or distributed databases, like Redshift and Snowflake, and experience with housing,accessing, and transforming data in a variety of relational databases.
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Specifically, this experience must be in writing Big Data data engineering jobs for large-scale data integration in AWS; prior experience in writing Machine Learning data pipelines using Spark programming language is an added advantage.
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The team works with various other business units within NVIDIA Software such as Graphics Processors, Mobile Processors, Deep Learning, Artificial Intelligence and Driverless Cars to cater to their infrastructure & system's needs.
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Big Data certifications (e.g., Cloudera Certified Professional (CCP): Data Engineer, Databricks Certified Professional Data Scientist). Deep knowledge of Big Data technologies such as Spark and Cloud ETL tools like Databricks, with a focus on scalability and real-time processing capabilities.
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Element 84 is a woman-owned small business that works with public, private, and non-profit sector clients to develop geospatial data processing pipelines & build software that helps answer big questions about our health, our infrastructure, and our changing planet.
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Big Data Technologies: Distributed Computing: Tools like Apache Spark, Hadoop. Software Engineering & DevOps: Version Control: Familiarity with tools like Git. Continuous Integration/Continuous Deployment: Knowledge of pipelines and tools like Jenkins, Travis CI. Containerization: Familiarity with Docker, Kubernetes, etc., for scalable deployment of data science applications.
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Join the Anheuser-Busch NAZ Tech Data and Analytics Team as a Machine Learning Engineer. Collaborate closely with other engineering guilds to integrate machine learning components into larger software systems and develop end-to-end solutions.
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learning software engineer processing big jobs Company: Lyft
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