Senior Lead Software Engineer, AI Modernization

JPMorgan Chase

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Jersey City, NJ
Posted: 30 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $219k

$184k most similar roles pay here $253k

This listing doesn't post a salary. Most similar roles pay $192,050–$246,150.

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 · Senior Lead Software Engineer, AI Modernization

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As an AI Modernization Senior Lead Software Engineer within the Wealth Management Technology team, you will join an agile team to build and deliver high-quality technology products. You will design and ship agentic systems that ingest legacy mainframe logic—including COBOL, JCL, DB2, and batch schedules—to produce verified, production-ready modern services in Kotlin/JVM. Your daily work involves building spec generation pipelines, RAG pipelines, and multi-agent orchestration layers while managing LLMOps for deployment, monitoring, and cost management. You will develop evaluation infrastructure to ensure calculation parity and extend ETL and CDC pipelines. Required skills include Python development, experience with distributed systems, and proficiency in tools like Claude Code, GitHub Copilot, and Cursor. The role focuses on the technical challenge of migrating complex financial calculations from legacy mainframe environments into modern, scalable architectures.

Skills

LLM RAG Prompt Engineering Python Kotlin JVM AWS EKS ECS Kubernetes Kafka PostgreSQL COBOL JCL DB2 LLMOps Event Sourcing CDC Airflow Temporal

What you'll do

Build and operate spec generation pipelines using RAG and chunking strategies to convert legacy mainframe data into structured specifications.
Design and implement multi-agent systems to translate legacy COBOL logic into modern Kotlin/JVM code.
Create automated test harnesses and parity testing frameworks to verify that migrated calculations match legacy results.
Develop and extend the target platform's calculation runtime to ensure deterministic and auditable execution.
Build and integrate ETL, change data capture (CDC), and event sourcing pipelines for end-to-end migrated workflows.
Manage LLMOps for the toolchain, including deployment, monitoring, cost management, and latency optimization.
Develop shared libraries, prompt templates, and orchestration patterns to scale AI capabilities across multiple domains.
Drive the adoption of AI-assisted engineering practices to improve code quality and delivery speed across teams.

What we're looking for

Formal training or certification in software engineering concepts.
5+ years of applied experience in software engineering.
Experience in Computer Science, Computer Engineering, Mathematics, or a related technical field.
Hands-on experience building LLM-based applications including agentic architectures, RAG pipelines, and prompt engineering.
Expert proficiency with AI-assisted development tools like Claude Code, GitHub Copilot, and Cursor.
Strong software engineering fundamentals in distributed systems, event-driven architectures, API design, and cloud platforms (AWS/EKS/ECS).
Strong experience with Python development in production environments.
Demonstrated ability to lead the use of enterprise-authorized AI tools while ensuring security, performance, and data sensitivity compliance.

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