Business Enablement - Senior Associate

JPMorgan Chase JPMorgan Chase · Banking · New York, NY +1 · Commercial & Investment Bank

Senior Associate role focused on driving AI adoption within JPMorgan Chase's Commercial & Investment Bank. The role involves working directly with business stakeholders to identify opportunities, build, and deploy AI solutions, particularly generative AI and agentic systems, using internal tooling. Responsibilities include translating business needs into technical requirements, prototyping, shipping production AI solutions, and contributing to AI playbooks and reusable libraries, with a strong emphasis on scaling and operationalizing solutions within a regulated financial environment.

What you'd actually do

  1. Work directly with stakeholders across CIB businesses, observing real workflows and helping identify high-leverage opportunities for GenAI to drive commercial impact
  2. Translate business domain context (across Markets, Banking, Payments & Securities Services) into clear technical requirements and, where possible, re-usable components for adoption at scale
  3. Prototype and ship production AI solutions against key business workflows using available internal tooling and systems; partner with Technology and CIB CDAO teams to deliver solutions in production that meet the firm's resilience, controls, and risk standards
  4. Contribute to end-to-end design, development, and iteration for assigned use cases — spanning data, retrieval, prompting, orchestration, evaluation, and deployment

Skills

Required

  • Knowledge of Financial Services, with exposure to one or more core CIB domains (e.g., capital markets, advisory, treasury services, payments operations, markets, or securities services)
  • BSc or MSc degree in Computer Science, Engineering, Mathematics, or a related quantitative field
  • Experience working with non-technical business stakeholders to scope, build, and ship technical solutions — solutions engineering, forward-deployed engineering, or analogous
  • Hands-on experience writing and shipping Python in a professional engineering environment
  • Experience designing, building, or deploying LLM-based applications: prompt engineering, evaluation, retrieval-augmented generation (RAG), and agentic frameworks/orchestration
  • Familiarity with the modern GenAI stack: LLM APIs, vector stores, embeddings, tool/function calling, multi-step agent design, and LLM evaluation
  • Strong analytical and problem-solving ability, with excellent attention to detail
  • Strong verbal and written communication skills; ability to communicate effectively with business users, engineering counterparts, and management

What the JD emphasized

  • AI Enablement
  • AI and accelerating the analytics agenda
  • integration of AI into business workflows
  • measurable commercial impact
  • traditional AI/ML and generative AI capabilities
  • high-impact AI use cases
  • GenAI strategy around LLMs and Agentic AI
  • reusing and scaling AI solutions
  • AI-first mindset
  • hands-on, applied AI role
  • rapidly build AI solutions
  • internal tooling
  • CIB AI playbook and reusable libraries
  • prompt libraries and skills
  • persona-level content is consistent, high-quality, and easy to discover
  • write quality code
  • design technical solutions
  • high-impact use cases
  • prototype through production
  • AI/ML tooling
  • deliver, scale, and operationalize your solutions
  • observing real workflows
  • identifying high-leverage opportunities for GenAI
  • drive commercial impact
  • Translate business domain context
  • clear technical requirements
  • re-usable components for adoption at scale
  • Prototype and ship production AI solutions
  • key business workflows
  • available internal tooling and systems
  • partner with Technology and CIB CDAO teams
  • deliver solutions in production
  • firm's resilience, controls, and risk standards
  • end-to-end design, development, and iteration
  • assigned use cases
  • data, retrieval, prompting, orchestration, evaluation, and deployment
  • Knowledge of Financial Services
  • exposure to one or more core CIB domains
  • BSc or MSc degree in Computer Science, Engineering, Mathematics, or a related quantitative field
  • Experience working with non-technical business stakeholders
  • scope, build, and ship technical solutions
  • solutions engineering, forward-deployed engineering, or analogous
  • Hands-on experience writing and shipping Python in a professional engineering environment
  • Experience designing, building, or deploying LLM-based applications
  • prompt engineering, evaluation, retrieval-augmented generation (RAG), and agentic frameworks/orchestration
  • Familiarity with the modern GenAI stack
  • LLM APIs, vector stores, embeddings, tool/function calling, multi-step agent design, and LLM evaluation
  • Strong analytical and problem-solving ability
  • excellent attention to detail
  • Strong verbal and written communication skills
  • ability to communicate effectively with business users, engineering counterparts, and management

Other signals

  • driving the adoption of AI
  • accelerating the analytics agenda
  • integration of AI into business workflows
  • delivering measurable commercial impact
  • advancing both traditional AI/ML and generative AI capabilities
  • identifying priorities and focusing on high-impact AI use cases
  • setting the GenAI strategy around LLMs and Agentic AI
  • reusing and scaling AI solutions
  • fostering a culture of innovation and an AI-first mindset
  • play a hands-on, applied AI role
  • rapidly build AI solutions using internal tooling
  • contribute to the CIB AI playbook and reusable libraries
  • write quality code and help design technical solutions
  • deliver, scale, and operationalize your solutions
  • Work directly with stakeholders
  • observing real workflows
  • identifying high-leverage opportunities for GenAI
  • Translate business domain context into clear technical requirements
  • Prototype and ship production AI solutions
  • partner with Technology and CIB CDAO teams to deliver solutions in production
  • meet the firm's resilience, controls, and risk standards
  • Contribute to end-to-end design, development, and iteration
  • spanning data, retrieval, prompting, orchestration, evaluation, and deployment