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Preferred skills: NLP, Text mining, Tableau, PowerBI, Databricks, Tensorflow. Preferred skills: NLP, Text mining, Tableau, PowerBI, Databricks, Tensorflow. Knowledge of Statistics, Gen AI, LLM, Python, Computer Vision, data visualization tools.
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Knowledge of Statistics, Gen AI, LLM, Sagemaker, Python, Computer Vision, data visualization tools. Spring boot, Microservices, Docker, Jenkins, Github, Kubernates and REST API's experience. We at Synergisticit understand the problem of the mismatch between employer's requirements and Employee skills and that's why since 2010 we have helped 1000's of candidates get jobs at technology clients like apple, google, Paypal, western union, Client, visa, walmart lab s etc to name a few.
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Develop and deploy machine learning models on GCP, leveraging tools such as AI Platform, TensorFlow, AutoML, and Vertex AI. Work closely with software engineers, data scientists, and business stakeholders to gather requirements and implement innovative solutions for data processing, analytics, and AI/ML projects.
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Experience with implementing machine learning pipelines using TensorFlow, Numpy, Scikit-Learn, OpenCV, DVC, W&B, TFLite, SNIPE, SHAP, UMAP, or ART. Experience with using deep learning frameworks, including Keras, TensorFlow lite, or PyTorch mobile.
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Experience with advanced machine learning techniques and packages (TensorFlow, PyTorch, Keras, Scikit-Learn, Pandas, NumPy, Seaborn, Orange3, SciPy, Matplotlib, Theano) Project Management certification (PMP, PRINCE) and/or Agile (e.g., APM, CSM, PMI-ACP, SAFe SM, CSPO.
$155,100 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Experience working with Deep Learning systems and deploying ML models (TensorFlow, DarkNet, PyTorch) Familiarity with both AWS and modern DevOps tooling (e.g., Terraform, Ansible, Docker, Kubernetes.
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You'll develop strategies to protect CDAO's technical offerings from insider threats, leveraging machine learning and deep learning techniques with tools like Jupyter, Spark, TensorFlow, or PyTorch.
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Experience with machine learning frameworks such as TensorFlow or Pytorch, Lightning, Mosaic ML etc. San Francisco, California (Hybrid On-Site): $213,400 - $243,500 for Lead Machine Learning Engineer.
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Use Deep Learning frameworks like PyTorch and Tensorflow to help our customers build computer vision models. Motion Imagery (commercial and USG FMV specs), or medical imagery (e.g. DICOM) We apply computer vision to numerous imagery and sensor types, such as satellite imagery, medical imaging, aerial video, synthetic aperture radar, X-Ray, and more.
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At least 5 years of hands-on experience adapting the following technologies in the development of novel technology-based approaches as part of research projects: NumPy, SciPy, scikit-learn, TensorFlow, Pytorch, Keras, Genism, OpenCV, etc.
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3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow. 11 West 19th Street (22008), United States of America, New York, New YorkLead Machine Learning Engineer ( Python, Spark, MLOPS, AWS.
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Proficiency in common geospatial software applications and tools, such as visual programming (JEMA, FADEMIST, ECO/ETAS), Python, SQL, Git, GIMS, AWS Sagemaker, A WS Cloud, ESRI ArcGIS, statistics (descriptive, Bayesian), Markov-Chain modeling, TensorFlow, Linear Algebra, R, SAS, NLP.
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Spring boot, Microservices, Docker, Jenkins, Github, Kubernetes, and REST API experience. We at Synergistic understand the problem of the mismatch between employer's requirements and Employee skills and that's why since 2010 we have helped 1000's of candidates get jobs at technology clients like Apple, google, Paypal, Western Union, Client, visa, Walmart lab s etc to name a few.
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Technical Proficiency: Expertise in Python, machine learning tools (Hadoop, TensorFlow, Apache Spark), and deep learning frameworks. Technical Proficiency: Expertise in Python, machine learning tools (Hadoop, TensorFlow, Apache Spark), and deep learning frameworks.
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Deep Learning: Understanding neural networks, backpropagation, architectures like CNNs, RNNs, transformers, and tools like TensorFlow, PyTorch, or Keras. Familiarity or experience with developing solutions for proposal within any of U.S. Government Agencies, to include but not limited to the following: Department of Transportation (e.g., FAA), NASA (including JPL), Department of Commerce (e.g., NOAA), or Department of Energy (e.g., HQ, NNSA.
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