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Strong expertise in generative AI techniques for summarization, Chatbot and data embedding using AWS, Microsoft Azure Open AI and other open source LLM models. Hands-on experience of machine learning methodologies including advanced analytics tools (such as R and Python) along with applied mathematics, ML and Deep Learning frameworks and libraries (TensorFlow, PyTorch, Keras) and ML techniques.
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Experience with Machine Learning tools (Tensorflow, Apache Spark) Experience with cloud technologies (AWS, Azure, GCP), OpenStack, and Kubernetes. The main needs for this position are AWS, Linux System Administration, (Python, Bash or Shell scripting.
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Software/Tools: AWS, Docker, Jira, VMware vCenter/vRealize, Kubernetes, Jenkins, Ansible, Prometheus, and Grafana; Cloud Native: AWS EC2, AWS CDK, S3, CloudWatch, CloudFront, CloudConfig, Elastic Container Services (ECS), DynamoDB, RDS, ElasticSearch, PowerBI, Tableau.
$110,000 - $150,000 a yearFull-timeExpandApply NowActive JobUpdated 6 days ago - UpvoteDownvoteShare Job
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Experience with machine learning algorithms and frameworks such as TensorFlow, Keras, PyTorch, Scikit-Learn, Knowledge of cloud computing platforms such as AWS, Azure, Google Cloud. With Cash App, anyone can easily send, spend, or invest their money in stocks or Bitcoin.
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Familiarity with cloud-based platforms (e.g. AWS, Azure) and big data processing tools (e.g. Apache Spark, Snowflake) Proficiency in programming languages such as Python, and expertise in relevant NLP and deep learning libraries and frameworks (e.g. TensorFlow, PyTorch, Spacy, langchain.
$82,000 - $236,500 a yearFull-timeRemoteExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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Proficient in common geospatial software applications and tools, such as visual programming (JEMA, FADE/MIST, ECO/ETAS), Python, SQL, Git, GIMS, AWS Sagemaker, AWS Cloud, ESRI ArcGIS, statistics (descriptive, Bayesian), Markov-Chain modeling, TensorFlow, Linear Algebra, R, SAS, NLP.
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Experience with at least one Deep Learning framework such as PyTorch or TensorFlow/Keras. Familiarity with cloud computing services (AWS, GCP, or Azure) A degree in Engineering, Statistics, Data Science, Applied Mathematics, Computer Science, Physics, Computational Biology, Computational Chemistry or related quantitative field (Masters or PhD preferred for Senior Manager direct hires.
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Python ecosystem preferred, R will be acceptable, machine learning libraries & frameworks (e.g. TensorFlow, PyTorch, scikit-learn) and familiar with data processing and visualization tools (e.g., SQL, Tableau, Power BI.
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Work closely with cloud providers (like Azure, AWS, GCP) and Meta's strategic partners on deep learning & LLM integrations. Solid understanding of at least one Deep Learning framework (PyTorch, Tensorflow or MXNet.
$111,000 - $168,000 a yearFull-timeExpandApply NowActive JobUpdated 6 days ago - UpvoteDownvoteShare Job
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Experience with any of the following desirable: REACT, Angular, JavaScript, RESTful Services, PostgreSQL, CSS, TensorFlow, OpenCV, xgboost, sklearn. Experience with Machine Learning and Data Analytics; Virtual Reality; Cloud technologies (AWS, Azure, GCP); and 3D Modeling a plus.
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Experience with anomaly detection and synthetic data strategies for machine learningExperience with computer vision (CNN, Transformer architectures) and image/video data preparation for machine learningHands-on experience with time series analysis and sequence modelingExperience with Cloud environments (Azure, AWS-SageMaker, S3, etc.
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Stellar presentation and written communication skillsFamiliarity with cloud computing platforms (e.g., AWS, Azure)Publications or contributions to open-source projects related to transformer architectures, vector databases, or language models/ natural language processing are a strong advantage.
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Experience with Sagemaker AWS, Kafka, Python, R, SQL and NoSQL Databases, Spark, Scikit-Learn, Keras/TensorFlow, PyTorch, Docker, CI/CD Pipelines, Git, or developing APIs. Experience with Sagemaker AWS, Kafka, Python, R, SQL and NoSQL Databases, Spark, Scikit-Learn, Keras/TensorFlow, PyTorch, Docker, CI/CD Pipelines, Git, or developing APIs.
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Dive deeper with cutting-edge tech like Spark, AWS, Azure, Cloudera, Kubernetes, and Google Cloud to build impactful solutions and gain real-time insights with Spark Streaming and Tensorflow.
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Experience working with a variety of relational SQL and NoSQL databases, big data tools: Hadoop, Spark, Kafka; a Linux environment; and at least one cloud provider solution (AWS, GCP, Azure). Experience with neural networks, deep learning, and reinforcement learning, using frameworks such as TensorFlow.
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