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Understanding of machine learning algorithms, deep learning techniques, and model evaluation metrics. Developing and deploying machine learning pipelines using Vertex AI and Kubeflow for data processing, feature engineering, model training, evaluation at scale in production on GCP ensuring data quality, integrity, and security.
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Proficiency in Python and experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX. 3-5 years of experience in applying deep learning methods to text data, including natural language processing, text classification, sentiment analysis, or other related applications.
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Investigate the machine learning methodologies, including deep learning, LLM, and graph NN, to address diverse challenges across different business verticals and customers.
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Knowledgeable in Prescriptive Modeling like Machine Learning, Deep Learning, Recommendation, Search, or NLP. This team supports the analytics and reporting needs across various commercial excellence functions including Marketing, Digital Commerce, Sales, Customer Experience (CX), Product Management, Corporate Strategy, Finance, and Pricing.
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We are interested in a variety of topics including data visualization and interactivity, applied machine learning and AI, AI bias and trust, big graphs, natural language processing, data mining, big data management, and heterogenous data analytics.
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Strong background in machine learning, deep learning, and probabilistic modeling. Proven experience (3-5 years) as a Data Scientist or Machine Learning Engineer with a focus on deploying Generative AI-based solutions in production environments.
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In this role you will be building and deploying machine learning models using both analytical algorithms and deep learning approaches. Apply data mining, NLP, and machine learning (both supervised and unsupervised) to improve relevance and personalization algorithms.
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1+ years of experience in machine learning techniques such as classification, regression, decision trees, neural nets, large language models, recommender systems, natural language processing, clustering, anomaly detection, sequential pattern discovery, text mining, and familiar with the latest trends and applications of generative AI.
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Candidates also need to have a publication track record showing research experiences that developed or applied at least one of the following techniques to solve a problem related to the broad application areas as listed above: signal processing/time series analysis, machine learning, deep learning, mathematical modeling, system identification, optimization, digital/mobile health, natural language processing, and data visualization.
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Strengths in Deep Learning, Machine Learning, Recommendation systems, Generative models, Statistical analyses. Foster a culture of collaboration, innovation, and continuous learning.
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Experience designing and developing deep learning, machine learning, optimization, and statistical models. The ideal candidate will have a strong background in machine learning, artificial intelligence, and cloud technologies.
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Solid experience developing and implementing Generative AI like transformers, LLM’s, text embedding’s and with strong understanding of deep learning techniques like GPTs. Proficient in Python and have experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, or Keras.
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Proven experience leading and managing machine learning, deep learning, and AI projects. Proven experience as a solution architect, software architect, or a similar role, with a focus on machine learning, and a deep understanding of the specific challenges and needs of the US Public Sector.
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Drive the adoption of cutting-edge technologies and methodologies in data engineering and AI, including machine learning (ML), data science, and advanced analytics. Exhibit knowledge and proficiency in specific AI and machine learning frameworks (LangChain, LlamaIndex, etc.
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Deep understanding of various AI technologies, including machine learning (ML), natural language processing, computer vision, deep learning, retrieval-augmented generation (RAG), and generative AI models, and programming languages such as Python, R, or Java.
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