Applied AI/ML Senior Associate, Small Business

JPMorgan Chase

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Work type
On-site
Location
New York, NY
Employment
Full-time
Posted
3 days ago
Freshness
Confirmed live today

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Similar $196k
$139k $253k
below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $151,217–$241,750.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1254 open roles on FindRole.

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At a glance

TL;DR · Applied AI/ML Senior Associate, Small Business

As an Applied AI/ML Sr. Associate - Small Business, you will join the Small Business Banking team to develop and operate agentic AI products supporting Small Business Lending. You will partner with Product and Engineering to translate business needs into production-ready systems by building and owning prompts, tool usage, memory, and routing for agent behavior. Your daily work involves building evaluations for task success, safety, and cost, while maintaining a continuous learning loop from production traces to failure analysis. You will utilize technologies including Python, FastAPI, Flask, PyTorch, and scikit-learn. Key responsibilities include building agentic systems with LangChain or LangGraph, fine-tuning SLMs, and implementing safety guardrails. You will also integrate relational and vector databases for retrieval-augmented generation while applying MLOps practices like CI/CD and observability.

What you'll do

  • Design, build, and deploy machine learning and generative AI solutions to address specific business needs.
  • Build agentic AI systems using LLM orchestration frameworks like LangChain or LangGraph.
  • Evaluate agent performance using golden sets, adversarial tests, and LLM-judge rubrics.
  • Fine-tune small language models (SLMs) and open-weight models for custom objectives.
  • Implement and monitor safety guardrails for prompt injection, PII handling, and spend limits.
  • Apply MLOps practices including CI/CD, versioning, observability, and prompt lifecycle management.
  • Integrate relational and vector databases to support retrieval-augmented generation (RAG).
  • Maintain a continuous learning loop from production traces to failure analysis and verified fixes.

What we're looking for

  • Bachelor’s, Master's, or PhD degree in computer science or a data science-related discipline.
  • 3+ years of industry experience.
  • Strong software engineering experience contributing to production systems.
  • Proficiency in Python, including asynchronous programming and API development with FastAPI, Flask, or similar frameworks.
  • Hands-on experience building and deploying generative AI or LLM applications in production.
  • Hands-on experience with LLM orchestration and agentic AI frameworks such as LangChain, LangGraph, or similar tools.
  • Experience implementing evaluations and guardrails using tools including deepeval and similar libraries.
  • Experience using tracing observability tools such as Arize Phoenix.

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