Applied Artificial Intelligence/ Machine Learning Lead - Vice President

JPMorgan Chase JPMorgan Chase · Banking · Jersey City, NJ +1 · Asset & Wealth Management

JPMorgan Chase is seeking an Applied AI/ML Vice President for their Global Private Bank. This role involves owning the full lifecycle of high-impact models for wealth management, lending, and advisory, from problem framing to production deployment. The position requires expertise in NLP, LLM fine-tuning, RAG, prompt engineering, and multi-step AI agents, with a focus on building AI-native capabilities at scale within a regulated financial services environment.

What you'd actually do

  1. Define and scope AI/ML problem statements in partnership with Private Bank business leads, translating ambiguous client or operational pain points into tractable modeling problems
  2. Design, build, and deploy end-to-end ML solutions — including generative AI, NLP, and classical machine learning— across client service, risk, and operational efficiency use cases
  3. Own model quality, evaluation frameworks, monitoring, drift detection, and iteration post-deployment
  4. Drive productionization and MLOps practices in collaboration with engineering, working across distributed data infrastructure
  5. Stay current on applied research; evaluate and adapt emerging techniques — new architectures, agentic frameworks, multimodal models — for relevance to the Private Bank's problem space and translate promising work into production-ready solutions

Skills

Required

  • Master's or PhD in Computer Science, Statistics, Applied Math, Data Science, or related quantitative field
  • At least 5 years of hands-on ML experience in production environments
  • Deep expertise in NLP, including modern LLM fine-tuning, RAG pipelines, prompt engineering and the design and deployment of multi-step AI agents
  • Strong Python skills; proficiency with PyTorch, TensorFlow, Scikit-learn and other libraries
  • Experience with large-scale data processing: Spark, Hive, SQL
  • Proven ability to communicate technical work to non-technical stakeholders

Nice to have

  • Financial services experience

What the JD emphasized

  • At least 5 years of hands-on ML experience in production environments
  • Deep expertise in NLP, including modern LLM fine-tuning, RAG pipelines, prompt engineering and the design and deployment of multi-step AI agents

Other signals

  • building AI-native capabilities
  • production deployment at scale
  • full lifecycle of high-impact models