Lead Software Engineer, AI Application

JPMorgan Chase

Confirmed live yesterday Low trust

Quick summary

Work type: On-site
Location: Jersey City, NJ
Posted: 38 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $211k

$187k most similar roles pay here $235k

This listing doesn't post a salary. Most similar roles pay $192,050–$230,400.

Based on 240 similar postings.

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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 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Lead Software Engineer, AI Application

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Lead Software Engineer - AI Application As a Lead Software Engineer within Corporate Technology, you will lead the architecture and hands-on implementation of scalable GenAI applications and agentic AI platforms for finance use cases. You will design cloud-native AWS services using containers and serverless patterns while building production-grade systems featuring agents, retrieval-augmented generation (RAG), guardrails, and tool orchestration. Your daily work involves developing advanced Python code, managing prompt lifecycles, and establishing evaluation, observability, and cost control frameworks for large language model workloads. You will utilize tools such as LangChain, LangGraph, Autogen, CrewAI, and AWS Bedrock to solve complex problems in a regulated enterprise environment. Additionally, you will mentor engineers, drive cross-team architecture standards, and integrate AI-assisted engineering practices into the software development lifecycle to improve code quality, reliability, and delivery speed across various business processes.

Skills

GenAI LLM LangChain LangGraph Autogen CrewAI Python AWS AWS Bedrock Google Vertex AI RAG Vector Databases Kubernetes Serverless Semantic Search

What you'll do

Lead the architecture and hands-on delivery of scalable agentic AI platforms for enterprise finance workflows.
Design and build production-grade AI systems including agents, memory patterns, guardrails, and tool orchestration.
Architect retrieval-augmented generation (RAG) solutions using embeddings, semantic search, and prompt management.
Engineer cloud-native AI services on AWS using containers, serverless patterns, and distributed data stores.
Establish evaluation, observability, and regression testing frameworks to ensure reliable agent behavior.
Optimize platform performance regarding latency, throughput, scalability, and cost controls.
Mentor engineers and drive cross-team technical standards for architecture and code quality.
Integrate AI capabilities into enterprise platforms through well-governed APIs and business process connections.

What we're looking for

Formal training or certification on software engineering concepts and applied experience.
Experience architecting and shipping production LLM applications, including agentic workflows and tool integration patterns.
Advanced Python programming skills with experience writing production quality code.
Experience building retrieval-augmented generation solutions using vector databases and managing prompt lifecycles.
Ability to build cloud-native services on AWS using containers and serverless designs.
Proven technical leadership skills, including mentoring engineers and driving architecture decisions for cross-functional stakeholders.
Demonstrated experience using AI-assisted software development tools while ensuring output correctness, performance, and security.
Experience building evaluation harnesses, Kubernetes deployment patterns, or applying guardrails in regulated environments (preferred).

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