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Experience with Machine Learning cloud technologies such as Azure Machine Learning (AML), AWS SageMaker or Spark ML. Experience with Big Data platforms such as Databricks or Apache Spark.
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Experience with the Azure Ecosystem (Azure Data Lake, Azure Data Factory, Azure Databricks, Azure Machine Learning (AML), Azure Cognitive Services, Azure Storage) Design, build, test and maintain the Machine Learning platform supporting Data Science initiatives.
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We are hiring a talented Senior Big Data Engineer to build cloud-based data pipelines for machine learning, data processing with Apache Spark, and database development. Knowledge and/or experience working with Apache Spark/Databricks.
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PNC Beeline VMS# 134263-1 Linux Systems Administrator ( + Cloudera CDP & Apache Spark support ) Proficiency in Apache Spark, including building Spark clusters using different cluster managers like Sparks' built-in standalone cluster manager, Apache Hadoop YARN, Apache Mesos, etc.
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Engage in the entire lifecycle of machine learning projects, from conceptualization and data processing to model development, deployment, and continuous optimization. - Previous experience as a software engineer, applied mathematician, or in roles involving DevOps, MLOps, Databricks, and Apache Spark is highly regarded.
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Founded in 2013 by the original creators of Apache Spark, Databricks has grown from a tiny corner office in Berkeley, CA to a global organization with over 1500 employees. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark, Delta Lake and MLflow.
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In-depth knowledge of Apache Spark, Pyspark and Databricks architecture. Oversee the implementation of data processing workflows, analytics, and machine learning models using Databricks.
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Security Engineering is an integral part of Trust & Safety and has a critical role to play in keeping customer data from bad actors. At Databricks, we are obsessed with enabling data teams to solve the world's toughest problems, from security threat detection to cancer drug development.
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For most teams this means a consistent balance of working from home and office that supports the needs of your role, experience level, and working style. Extensive experience working with machine learning models with respect to deployment, inference, tuning, and measurement required.
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Data Science and Machine Learning Frameworks Apache Spark / MLlib, TensorFlow, Scikit-learn, etc. Working with a team of data scientists, machine learning engineers, software engineers and QA engineers to develop AI/ML services in support of our business operations.
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Establish and set up model life cycle management with tools like MLFlow, etc· Developing and deploying Spark/Databricks jobs with enterprise tool stack including Jenkins, GitHub Actions· Deployment utilizing containerization solutions like Docker and Kubernetes· Experience with AWS cloud services and running Apache Spark applications.
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Leverage data science frameworks like TensorFlow, PyTorch, and Spark ML to build robust and scalable machine learning solutions. JOB RESPONSIBILTIES:Develop and optimize machine learning algorithms and models using Python, with a focus on performance and scalability, working very closely with the Data Scientists.
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Experience with big data technologies and tools such as Apache Spark, Hadoop, Apache Cassandra, and distributed computing frameworks for handling large-scale datasets. years in a technical role delivering projects in Data Analytics, Machine Learning, Cloud Computing Platforms, and Service-oriented architecture, with a preference for Large Language Model (LLM.
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In-depth understanding of various machine learning techniques, including supervised and unsupervised learning, deep learning, reinforcement learning, and natural language processing.
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Familiarity with Big Data technologies like Snowflake, Apache Beam/Spark/Flink, and Databricks. Apache Spark, Kubeflow, Dataflow, Kubernetes, Kafka, Pub/Sub, and Flask. Embark on a leadership role within the Blue Yonder Data Science and Machine Learning team as a Staff Data Scientist, steering our machine learning platform that processes real-time data and powers deep learning models, generating billions of predictions daily.
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