Job Description:
Hands-on Development:
Develop and implement machine learning models and algorithms, including supervised, unsupervised, deep learning, and reinforcement learning techniques.
Implement generative AI solutions using technologies like RAG (Retrieval-Augmented Generation), Vector DBs, and frameworks such as LangChain and Hugging Face.
Utilize popular AI/ML frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn.
Design and deploy NLP models and techniques, including text classification, RNNs, CNNs, and Transformer-based models like BERT.
Technical Proficiency:
Demonstrate strong programming skills in languages commonly used for data science and ML, particularly Python.
Leverage cloud platforms and services for AI/ML, especially AWS, with knowledge of AWS Sagemaker, Lambda, DynamoDB, S3, and other AWS resources.
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