Machine Learning Engineer - RAGs

July 27

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Machine Learning • Data Engineering • Data Analytics • Artificial Intelligence • Data Science

51 - 200

Description

• Design, develop, and optimize RAG models that combine retrieval-based and generation-based approaches to solve complex problems. • Enhance the performance of RAG models through innovative algorithmic techniques and fine-tuning. • Fine-tune and adapt LLMs for specific tasks and domains within the RAG framework. • Work with cross-functional teams to integrate RAG models into production environments and ensure seamless deployment. • Utilize machine learning methods and advanced techniques like LLMs to create effective AI solutions. • Design, deliver, and maintain features such as predictive analytics, automated risk assessments, intelligent data extraction, and personalized insights. • Write well-designed, maintainable, and testable code. Create clear and comprehensive design documentation. • Prioritize customer needs and experiences in all development efforts. • Designed and developed frameworks for GenAI products, such as search interfaces, bots, and summarizers.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field. • 3-4 years of hands-on experience in developing and deploying machine learning models. • Strong Python skills are required. • 3+ years of experience with production NLP and deep learning models using PyTorch/TensorFlow. • Proven experience utilizing cutting-edge methodologies such as Retrieval-Augmented Generation (RAG) and other innovative techniques to enhance model performance and create sophisticated AI solutions. • Proficiency in various prompting techniques with a clear understanding of the tradeoffs between prompting and fine-tuning. • Experience with cloud computing platforms (AWS, GCP) or equivalent on-premise platforms.