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Familiar with PySpark dataframe and data processing libraries, machine learning frameworks (like Tensorflow, Keras or PyTorch), and other machine learning libraries.
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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.
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Model Integration & Scalability: Collaborate with cross-functional teams to integrate data science solutions into scalable, production-grade systems using open-source frameworks like Apache Spark and containerization technologies like Docker.
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Experience with Apache Airflow and Apache Spark; Spark performance turning. Able to explore new tools, technologies that will help to improve ETL platform performance and machine learning operations.
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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. - Conduct ongoing research to stay abreast of the latest developments in machine learning, deep learning, and data science, and apply this knowledge to enhance project outcomes.
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Experience with Apache Spark, Docker, Kubernetes, MySQL, NoSQL, MongoDB, Redis, React, and Nginx. You should have senior level knowledge to support Apache Spark, Docker, Kubernetes, MySQL, Redis, NoSQL, MongoDB, Prometheus, React, and Nginx (tech stack.
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Experience with large-scale data processing tools like Apache Spark and cloud-computing environments like Databricks and Snowflake. Python strongly preferred, including experience with current machine learning frameworks and tools such as Pandas, NumPy, scikit-learn, TensorFlow, PySpark MLlib.
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Experience with data management technologies such as Databricks, Apache Spark, Hadoop, Kafka. 8+ years of experience in developing business applications for Machine Learning and Data Science workloads.
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Leverage data science frameworks like TensorFlow, PyTorch, and Spark ML to build robust and scalable machine learning solutions. ROLE SUMMARY: Join the Anheuser-Busch NAZ Tech Data and Analytics Team as a Machine Learning Engineer.
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As a member of the Data Engineering team, the Machine Learning Engineer will work closely with business domain experts and Data Scientists to solve real-world oil and gas midstream problems using advanced analytics, machine learning, and artificial intelligence.
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You will work closely with data scientists, machine learning engineers, and other software engineers, using the latest tools and technologies to develop analytic solutions and integrate analytic models with existing business applications.
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The Machine Learning Engineer will be responsible for architectural design and planning, advanced data pipelines, model integration and optimization, scalability, performance and research and innovation supporting production AI systems.
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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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Experience working with machine learning frameworks (e.g. scikit-learn, XGBoost, PyTorch, TensorFlow) and distributed big-data tools (e.g. Spark) is a plus. 2+ years of professional experience as a MLOps Engineer, Data Engineer, or equivalent role, focusing on machine learning and/or ML infrastructure.
$110,000 - $142,000 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Proven proficiency in data pipeline tools (e.g., Apache Airflow, Apache Kafka, Apache Spark) - Knowledge of streaming data processing and real-time data pipelines.
$140,000 - $180,000 a yearFull-timeExpandApply NowActive JobUpdated 6 days ago
machine learning apache spark data processing electronic medical records jobs Title: engineer
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