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Experience with data management technologies such as Databricks, Apache Spark, Hadoop, Kafka. JOB SUMMARY As a Senior Machine Learning Engineer, you will work on building AI/ML solutions across a wide range of business applications within The Friedkin Group of companies.
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Experience with Spark jobs for data processing, analysis, and machine learning tasks using Databricks. Develop and optimize Spark jobs for data processing, analysis, and machine learning tasks.
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We're looking for someone who has a passion for building scalable machine learning and MLOps platforms using technologies such as Python, Databricks, Spark, AWS, Airflow, Terraform, etc.
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Sr. AWS Cloud Engineer w/ Machine Learning Ops. Experience with building data pipelines in getting the data required to build, deploy and evaluate ML models, using tools like Apache Spark, AWS Glue or other distributed data processing frameworks.
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Architect and develop large-scale, distributed data processing pipelines using technologies like Apache Spark, Apache Beam, and Apache Airflow for orchestration. Proven expertise in Apache Spark, Apache Beam, and Airflow, with a deep understanding of distributed computing and data processing frameworks.
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Familiarity with streaming data processing, real-time analytics, and machine learning pipelines. Experience in stream / data processing technologies like Kafka, Spark, Google BigQuery, Google Dataflow, HBase.
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This is a hybrid position that requires a candidate local to Austin, TX Responsibilities: 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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Experience in Big Data ecosystem : ETL, tooling of Big Data Platform (Apache Spark, Airflow), Datalake, Synapse or Snowflake. As a Senior Reliability Engineer, you will play a critical role in ensuring the robustness, availability, and performance of our cutting-edge Data Engineering and Machine Learning Platforms.
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Exposure to big data approaches: PySpark, Apache Spark, Azure Databricks, Azure SQL, Azure ML, Java. Spectrum of offerings across basic RPA to machine learning, cognitive automation, and Quantum Computing capabilities.
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Develop and optimize ETL processes using Databricks and related tools like Apache Spark. Good understanding of spark architecture with Databricks structured streaming, setting up Azure with Databricks, managing clusters in Databricks.
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Extensive experience with Apache Spark and Databricks (or similar data processing frameworks). Design, develop, and implement efficient and scalable data pipelines using Apache Spark on Databricks.
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Big Data Technologies:Distributed Computing: Tools like Apache Spark, Hadoop. Transform your career to the next level with GDIT as a Solutions Architect where you can elevate your skills in Data Science, Machine Learning, or leading AI techniques and technology expertise into solutions in support of our Government customers.
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Experience with machine learning related open source libraries including, but not limited to: Hadoop, Spark, SciKit-Learn, TensorFlow, etc. We are on the forefront of CBRN defense and we are looking for talented Data Scientists that have applied experience in the fields of artificial intelligence, machine learning and/or natural language processing to join our team.
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Experience with high speed distributed computing frameworks such as AWS EMR, Hadoop, HDFS, S3, MapReduce, Apache Spark, Apache Hive, Kafka Streams, Apache Flink etc. Experience with high speed distributed computing frameworks such as AWS EMR, Hadoop, HDFS, S3, MapReduce, Apache Spark, Apache Hive, Kafka Streams, Apache Flink etc.
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Proven proficiency in data pipeline tools (e.g., Apache Airflow, Apache Kafka, Apache Spark) - Familiarity with machine learning and AI platforms. Extensive experience with data warehousing and data lake technologies (e.g., Snowflake, BigQuery, Redshift.
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