Applied AI Ml-vice President

JPMorgan Chase JPMorgan Chase · Banking · New York, NY +1 · Consumer & Community Banking

As an Applied AI/ML Vice President in the AI for Operations organization at JPMorgan Chase, this role focuses on designing, building, and scaling NLP solutions to improve customer and internal agent experiences. The position involves hands-on work across the model lifecycle, translating operational needs into production-grade language capabilities like intelligent search, summarization, classification, and next-best-action recommendations. The role emphasizes applying technical judgment to choose appropriate methods (including LLMs), evaluating performance, and ensuring solutions are reliable, secure, and scalable.

What you'd actually do

  1. Oversee the analysis of complex datasets to inform decisions on real-world applications.
  2. Lead the development and implementation of models and algorithms to enhance existing systems, processes, and products.
  3. Supervise data analysis activities and ensure effective visualizations are provided.
  4. Ensure the writing and deployment of software code in production systems is efficient and meets standards.
  5. Anticipate risks associated with machine learning solutions and prediction/classification systems and strategize mitigation.

Skills

Required

  • Master's degree in computer Science, Machine Learning, or a related field with 4+ years experience OR Ph.D in computer Science, Machine Learning, or a related field with 2+ year of experience.
  • Expertise in one or more of the following areas: machine learning, Graph learning, recommendation systems, network analysis, natural language processing, Reinforcement learning, MLOps, Gen AI, LLMs.
  • Solid understanding of core CS concepts, including common data structures and algorithms.

Nice to have

  • Experienced in conducting design and code reviews.
  • Proficient in cloud environments such as AWS, GCP, or Azure.
  • Experienced in managing and deriving insights from large, both unstructured and structured datasets.

What the JD emphasized

  • reliable, secure, and scalable
  • cutting-edge Natural Language Processing (NLP) solutions
  • customers
  • internal agents
  • intelligent search, summarization, classification, and next-best-action recommendations
  • complex datasets
  • models and algorithms
  • software code in production systems
  • machine learning solutions
  • prediction/classification systems

Other signals

  • LLM-based methods
  • cutting-edge Natural Language Processing (NLP) solutions
  • improve experiences for both customers and internal agents
  • intelligent search, summarization, classification, and next-best-action recommendations