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JPMorgan AWM Applied AI ML Sr Associate India, Karnataka, Bengaluru 520352651
Today
ob Responsibilities
Develop and implement GenAI and Agentic AI solutions using Python to enhance automation and decision-making processes.
Apply large language models (LLMs), machine learning (ML) techniques, and statistical analysis to enhance informed decision-making and improve workflow efficiency, which can be utilized across investment functions, client services, and operational process.
Collect and curate datasets for model training and evaluation.
Perform experiments using different model architectures and hyperparameters, determine appropriate objective functions and evaluation metrics, and run statistical analysis of results.
Monitor and improve model performance through feedback and active learning.
Stay up-to-date with the latest research in LLM, ML and data science. Identify and leverage emerging techniques to drive ongoing enhancement.
Required qualifications, capabilities, and skills
Advanced degree (MS or PhD) in a quantitative or technical discipline or significant practical experience in industry.
Minimum of 4 years of experience in applying NLP, LLM and ML techniques in solving high-impact business problems, such as semantic search, information extraction, question answering, summarization, personalization, classification or forecasting.
Advanced python programming skills with experience writing production quality code
Good understanding of the foundational principles and practical implementations of ML algorithms such as clustering, decision trees, gradient descent etc.
Hands-on experience with deep learning toolkits such as PyTorch, Transformers, HuggingFace.
Strong knowledge of language models, prompt engineering, model finetuning, and domain adaptation.
Familiarity with latest development in deep learning frameworks.
Preferred qualifications, capabilities, and skills
Prior experience of developing solutions for Financial domain
Exposure to distributed model training, and deployment
Familiarity with techniques for model explainability and self validation
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