VP - Genai Quant Developer

Bank of America Bank of America · Banking · New York, NY

VP role focused on developing and deploying LLM-powered applications and agentic systems for the Rates business within Bank of America. This includes workflow automation, trade idea generation, and building multi-step reasoning systems with tool use. The role also involves researching and evaluating new AI/ML methods, partnering with tech and business stakeholders, and ensuring AI solutions are safe and compliant. Additionally, it includes core quantitative development for linear rates derivatives pricing and risk models.

What you'd actually do

  1. Design, prototype, and deploy LLM-powered applications for the Rates business, e.g.: Workflow automation for pricing, risk checks, data QA, and post-trade analysis. Trade idea generation and scenario tooling,
  2. Build agentic workflows (and/or similar orchestration frameworks) to create reliable, multi-step reasoning systems with tool use (pricing engines, risk calculators, data APIs).
  3. Research and evaluate new models/methods (prompting, fine-tuning, embeddings, evaluation harnesses, guardrails) and develop lightweight prototypes that can be productionized.
  4. Partner with tech, support partners and the business to ensure AI solutions are safe, compliant, and aligned with front-office needs, including appropriate monitoring and model risk controls.
  5. Develop and enhance pricing and risk models for linear rates derivatives (e.g., swaps, FRAs, futures, treasuries) and associated curve/market data analytics.

Skills

Required

  • Python
  • LLMs and agent frameworks
  • AI/ML
  • Data & infrastructure
  • Software engineering

Nice to have

  • Quant finance

What the JD emphasized

  • production-quality engineering practices
  • LLMs and agent frameworks: LangGraph, LangChain, OpenAI-compatible APIs, multi-agent orchestration
  • AI/ML: deep learning, NLP, embeddings, RAG, classical ML
  • Data & infrastructure: SQL, vector databases, time-series data, cloud-native or distributed systems
  • Software engineering: APIs, model deployment, monitoring, and scalable architecture

Other signals

  • LLM-powered applications
  • agentic workflows
  • tool use
  • prompting, fine-tuning, embeddings, evaluation harnesses, guardrails