AI/ML Senior Lead Software Engineer, Agentic Pricing

JPMorgan Chase

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Quick summary

Work type: On-site
Location: Jersey City, NJ
Employment: Full-time
Posted: 2 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $202k

$141k $254k

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This listing doesn't post a salary. Most similar roles pay $169,162–$235,750.

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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 1239 open roles on FindRole.

Listed pay typically runs $136,000–$207,500 across 6 roles with salary data.

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At a glance

TL;DR · AI/ML Senior Lead Software Engineer, Agentic Pricing

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The AI/ML Senior Lead Software Engineer - Agentic Pricing joins the Commercial and Investment bank Digital & Platform Services, Data Analytics team. This role involves designing and shipping production agents on NEO, building retrieval systems using Graph RAG, and designing agent memory with episodic and semantic nodes. You will compose multi-agent workflows using A2A, integrate tools through MCP servers, and build task-level evaluations and regression suites. Key technologies include Python, Kubernetes, Amazon EKS, SQL, NoSQL, and cloud platforms like AWS and Azure. You will manage organizational context, ensure security through mTLS and runtime isolation, and apply deep knowledge of information retrieval and statistics. The work focuses on developing LLM-powered agentic applications within the financial services and payments domain, ensuring production stability and security.

Skills

Python Kubernetes AWS Azure Amazon EKS RAG LLM Machine Learning OpenSearch Redis Snowflake Databricks Go Rust TypeScript Next.js CI/CD Graph Databases Splunk

What you'll do

Design and ship production agents on NEO from prototype through production.
Build production-grade retrieval systems using Graph RAG, vector search, and grounding strategies.
Design agent memory systems including episodic and semantic memory with specific recall and decay policies.
Manage organizational context to ensure agents reason only over authorized data.
Compose multi-agent workflows using A2A and integrate tools via MCP servers.
Build and run task-level and end-to-end agent evaluations, regression suites, and safety gating.
Deploy and operate solutions on public cloud using SDLC, security, and observability practices.
Provide technical guidance to business teams, contractors, and vendors.

What we're looking for

Formal training or certification in software engineering concepts.
5+ years of applied experience in software engineering with at least 4 years focused on AI/ML solutions.
Advanced programming skills in Python, including deep knowledge of data structures, algorithms, machine learning, and statistics.
Hands-on experience building production LLM-powered or agentic applications with tracing, evaluations, and guardrails.
Deep proficiency with Kubernetes, Amazon EKS, micro-VM isolation, and designing defense-in-depth security architectures.
Practical RAG experience with embeddings and vector stores, plus expert knowledge of AWS, Azure, or Kubernetes.
Proficiency in the Software Development Life Cycle, agile methodologies, CI/CD, and real-time processing using SQL and NoSQL stores.
MS in Computer Science, Statistics, Mathematics, Machine Learning, or related field (preferred).