Solution Analyst II

JPMorgan Chase JPMorgan Chase · Banking · GLASGOW, LANARKSHIRE, United Kingdom · Asset & Wealth Management

This role focuses on integrating large language models (LLMs) into enterprise workflows, specifically within Asset & Wealth Management. The analyst will bridge the gap between business and technical teams, translating requirements into solutions. Key responsibilities include contributing to data-driven decision-making, collaborating on architecture designs, managing dependencies, and turning complex problems into structured requirements. Preferred qualifications include experience with LLM integration via APIs, RAG patterns, prompt engineering, evaluation, and LLM orchestration frameworks like LangChain.

What you'd actually do

  1. Turn complex problems into structured requirements and solution approaches by eliciting, analyzing, and documenting business and end-to-end data flows.
  2. Strengthen decision making by extracting, interpreting, and communicating insights from large and varied data sets, using appropriate analytics techniques to surface actionable findings.
  3. Contribute to technical direction by partnering with cross functional teams to provide input on architecture and operating model considerations, ensuring solutions align with business strategy and technical constraints
  4. Support delivery discipline by helping manage dependencies, participating in change control, and adapting quickly as priorities evolve in a fast-paced environment
  5. Advance continuous improvement by identifying process gaps, recommending enhancements, and applying Solutions Analysis principles and practices to raise execution quality

Skills

Required

  • solutions analysis
  • eliciting and documenting business and data flow requirements
  • data fluency
  • data extraction
  • data interpretation
  • making data-informed decisions
  • data visualization
  • analytics
  • vendor products
  • managing vendor relations
  • written communication skills
  • translate complex information for diverse stakeholder audiences

Nice to have

  • Experience integrating large language models into enterprise workflows through APIs (for example, Claude API and OpenAI APIs)
  • Practical understanding of retrieval-augmented generation patterns, including embeddings, vector stores, and grounding techniques designed to reduce hallucinations
  • Proficiency in prompt engineering and evaluation, including defining acceptance criteria for non-deterministic outputs, designing human-in-the-loop review processes, and applying risk controls for model failure modes
  • Familiarity with LLM orchestration frameworks (for example, LangChain)
  • Background working in enterprise environments with Agile delivery methods (Scrum and/or Kanban)
  • JIRA
  • Confluence
  • Python
  • SQL
  • REST APIs
  • data visualization platforms

What the JD emphasized

  • Experience integrating large language models into enterprise workflows through APIs (for example, Claude API and OpenAI APIs)
  • Practical understanding of retrieval-augmented generation patterns, including embeddings, vector stores, and grounding techniques designed to reduce hallucinations
  • Proficiency in prompt engineering and evaluation, including defining acceptance criteria for non-deterministic outputs, designing human-in-the-loop review processes, and applying risk controls for model failure modes
  • Familiarity with LLM orchestration frameworks (for example, LangChain)

Other signals

  • integrating large language models into enterprise workflows
  • retrieval-augmented generation patterns
  • prompt engineering and evaluation
  • LLM orchestration frameworks