Applied Gen AI VP - Wholesale Credit Risk Quantitative Research

5 - 9 years

5 - 9 Lacs

Posted:1 week ago| Platform: Foundit logo

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Job Type

Full Time

Job Description

Are you looking for an exciting opportunity to solve large scale business problems using Gen AI Join our dynamic team to tackle large-scale business challenges using Generative AI. As part of the Wholesale Credit Risk Quantitative Research - Applied AI/ML team, you will develop innovative AI solutions leveraging the firms extensive data resources. Your focus will be on creating tools based on Large Language Models (LLMs) to enhance the End-to-End credit risk process across Wholesale.

This role offers an exciting opportunity to innovate and make a significant impact in the field of credit risk management. If you are passionate about AI and eager to work on cutting-edge solutions, we encourage you to apply.

Key Responsibilities

  • Develop and implement AI solutions to address business challenges.
  • Collaborate with cross-functional teams to translate requirements into technical solutions.
  • Formulate risk strategies to enhance risk monitoring using diverse data sources.
  • Manage the full lifecycle from Proof of Concept to production-ready solutions, including stakeholder presentations and post-implementation monitoring.
  • Ensure the performance and reliability of deployed solutions.
  • Stay informed on the latest AI/ML advancements.
  • Lead the development and rapid deployment of AI solutions influenced by macro-economic factors and current events.

Requires Qualifications, Skills and experience

  • Advanced degree in Data Science, Computer Science, Engineering, Mathematics, or Statistics.
  • Minimum of 5 years of experience in applied AI/ML.
  • Strong understanding and practical experience with Machine Learning; expertise in LLM/NLP is highly preferred.
  • Proficiency in modern analytic and data tools, especially Python/Anaconda, TensorFlow, Keras/PyTorch, Spark, and SQL. Cloud experience is a plus.
  • Experience in model implementation and production deployment is preferred.

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