Drimva

JPMorgan Chase

AI/ML Senior Lead Software Engineer - Agentic Pricing

$176K–$260K

Jersey City, NJ · Senior · Full-time · Posted Oct 28, 2026

Experience
5+ years applied experience, with at least 4 years working on AI/ML solutions.
Education
Formal training or certification on software engineering concepts; preferred MS in Computer Science, Statistics, Mathematics, Machine Learning, or related field (or equivalent experience).

About the job

Leads the design, delivery, and operation of production AI agents and agentic pricing solutions across JPMorganChase’s Commercial and Investment Bank.

What you'll do

  • Design and ship production agents, including retrieval, memory, context management, and multi-agent workflows.
  • Build agent evaluations and operate solutions with security, resiliency, and observability practices.
  • Partner with business and product teams to deliver supported agents and provide technical guidance.

What they ask for

  • Formal training or certification in software engineering concepts and 5+ years of applied experience, including at least 4 years working on AI/ML solutions.
  • Strong Python programming skills and hands-on experience delivering system design, application development, testing, and operational stability.
  • Production experience with LLM-powered or agentic applications, including tracing, evaluations, and guardrails; practical RAG experience.
  • Deep proficiency with Kubernetes and Amazon EKS, and expertise in at least one of AWS, Azure, or Kubernetes.

Nice to have

  • MS in a related field (or equivalent experience); agent frameworks, A2A, MCP, agent memory, or organizational context management experience.
  • Knowledge of graph retrieval, LLM fine-tuning, JavaScript/TypeScript frameworks, financial or payments domain, or Go or Rust.

Pay

$175,750.00-$260,000.00 USD base salary

Tools

Python, NEO, A2A, MCP, Bitbucket, Confluence, Databricks, Kubernetes

AI focus

Build production agents for pricing, including Graph RAG, agent memory and context management, multi-agent workflows, and evaluations.

Model-written summary · the original posting is authoritative

First seen Oct 29, 2026 · Last verified Nov 6, 2026 (UTC)