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Natural Language Processing techniques and algorithms · Strong coding experience in Python, Pyspark, Keras/tensorflow · Experience with SQL & at least one NoSQL databases · Experience of working on any of cloud platform (AWS, Azure, IBM etc.
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Experience with ML frameworks like pytorch or TensorFlow is a plus. Familiar with at least one cloud provider like AWS, Azure, or GCP. Together, we develop and maintain the foundational platform that allows ADI to rapidly prototype and deploy sensor-to-cloud innovations.
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Experience with machine learning frameworks (e.g., TensorFlow, scikit-learn) and data visualization tools (e.g., Tableau, Power BI). Familiarity with cloud platforms (e.g., AWS, Google Cloud) and big data technologies (e.g., Hadoop, Spark.
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Work with our data engineering team to develop and maintain data pipelines, ETL processes, databases and data warehouses for the firm's workplace data. Proficiency in programming languages such as Python, R, and SQL.
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4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow. Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform.
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Experience with deep learning frameworks (TensorFlow, PyTorch) and classical ML libraries (Scikit-learn). Familiarity with cloud platforms (AWS, GCP, or Azure) and deploying machine learning models.
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Familiarity with Machine Learning/Data Science concepts and experience working with libraries like PyTorch, TensorFlow. We're enhancing our applications with cloud-native capabilities, including data at scale, edge computing, AI-based solutions, and advanced 3D modeling and graphics.
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Proficiency in Python and machine learning frameworks such as TensorFlow, Pytorch, and/or JAX. Experience developing, debugging, and applying models using modern deep learning frameworks on GPUs in cloud environments.
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Experience with cloud platforms such as Google Cloud Platform and Microsoft Azure, a plus. Expertise with Python libraries like Tensorflow/Theano/Keras, Numpy, Scipy, Pandas, Matplotlib, and Seaborn, a plus.
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High proficiency in developing and/or implementing AI/ML models in Python and R, particularly generative protein design, knowledge graphs and semantic modeling and inference, supervised/unsupervised learning, and LLMs. Experience with ML frameworks for deep learning (e.g. PyTorch, TensorFlow, Keras, MXNet) & cloud platforms.
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Additional Information: Wolters Kluwer offers great benefits and programs to help meet your needs and balance your work and personal life, including Medical, Dental, & Vision Plans, 401(k), FSA/HSA, Commuter Benefits, Tuition Assistance Plan, Vacation and Sick Time, and Paid Parental Leave.
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Experience using Databricks, PySpark / Spark, Azure Cloud, Kubernetes, Docker, PyTorch, Keras, Tensorflow. The Data Scientist will be responsible for Exploratory Data Analysis (EDA), feature engineering, feature selection, real-time streaming pipeline engineering, online/offline feature store curation, iterating various modeling approaches, evaluating error metrics, deploying Machine Learning models to the cloud, creating compelling visualizations and communicating findings effectively to large audiences.
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3+ years of experience in in core programming languages and data science packages (Python, Keras, Tensorflow, PyTorch, Pandas, Scikit-learn, Jupyter, etc.) 3+ years of Experience with cloud deployment (AWS, Azure, GCP), such as building and scaling in AWS SageMaker.
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Recent experience with AI/ML toolkits such as AWS SageMaker, PyTorch, TensorFlow or Keras. Familiarity with cloud platforms such as AWS, Azure or GCP. Proficient in working with LLM+RAG agents and NLP solutions.
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Experience with AI and Machine Learning platforms, tools, and frameworks, such as: PyTorch, LLaMA.cpp, vLLM, fsdp, deepspeed, Kubeflow, and Tensorflow. Red Hat is the world’s leading provider of enterprise open source software solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies.
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