Senior Lead Software Engineer- Python Distributed Development and AI Modernization

JPMorganChase; Jersey City (NJ); On-site; USD 170,000 - 210,000; Full time

Job summary

JPMorgan Chase in Jersey City seeks a Senior Lead Software Engineer to design, build, and ship agentic systems that convert legacy COBOL/JCL into modern, production-ready services. You’ll lead multi‑domain efforts with SMEs in Credit and Taxes, extend ETL/CDC pipelines, and drive LLMOps for reliable, auditable solutions.

The role emphasizes scalable automation, rigorous testing, and governance of AI practices in a fast-paced financial tech environment.

Qualifications

• Formal training or certification in software engineering concepts and 5+ years of applied experience.
• Hands-on experience building LLM-based applications – agentic architectures, RAG pipelines, prompt engineering, and evaluation frameworks.
• Strong software engineering fundamentals: distributed systems, event-driven architectures, API design, testing practices, and cloud platforms (AWS/EKS/ECS).
• Expert proficiency with AI-assisted development tools (Claude Code, GitHub Copilot, Cursor) as core daily workflow.
• Strong experience with Python development in production environments.
• Demonstrated ability to operate and debug complex systems – you own what you ship.
• Clear communicator who can articulate technical trade-offs to both engineers and business stakeholders.
• Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment, with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
• Experience in Computer Science, Computer Engineering, Mathematics, or a related technical field.

Responsibilities

• Build and operate the specification generation pipeline – implement artifact ingestion (COBOL source, JCL, job schedules, DB2 schemas, SME‑captured knowledge).
• Develop agentic workflows for code translation and migration – translate legacy logic into Kotlin/JVM targets with guardrails.
• Build evaluation and verification infrastructure – create automated test harnesses and parity testing against legacy results.
• Contribute to the standard calculation runtime – extend the target platform with deterministic, auditable execution.
• Partner with domain SMEs across Credit, Money Market, Mutual Funds, Statements & Tax, and IBOR to validate agent outputs.
• Extend ETL and CDC pipelines for agent workflows – implement event sourcing and data pipelines.
• Operate AI systems in production – own LLMOps: deployment, monitoring, cost management, latency optimization.
• Iterate rapidly and ship continuously – build reusable libraries, templates, orchestration patterns for four core processing domains.
• Drive adoption and governance of approved AI-assisted engineering practices across teams to improve code quality and delivery speed.
• Apply SDLC/TLM tooling knowledge to improve automation at scale.

Skills

Python development

LLM‑based apps

Distributed systems

API design

Testing practices

AWS/EKS/ECS

AI‑assisted tooling

Leadership & communication

Responsible AI practices

Education

BS/MS in CS/CE/Math or related field

Tools

Kubernetes (EKS)

Airflow/Temporal

Kafka

PostgreSQL

Job description

Job Description

Be an integral part of an agile team that constantly pushes the envelope to enhance, build, and deliver top‑notch technology products. As a Senior Lead Software Engineer – Python Distributed Development and AI Modernization at JPMorgan Chase within the Consumer and Community Bank – Wealth Management Technology, you will design, build, and ship agentic systems that ingest decades of mainframe logic and produce verified, production‑ready modern services. You will work directly with domain SMEs to turn legacy COBOL, JCL, DB2, and batch schedules into structured specifications, then drive those specifications through agent‑accelerated delivery into the target platform.

Job Responsibilities

• Build and operate the specification generation pipeline – implement artifact ingestion (COBOL source, JCL, job schedules, DB2 schemas, SME‑captured knowledge), chunking strategies, and RAG pipelines that produce structured calculation and workflow specifications validated by domain experts.
• Develop agentic workflows for code translation and migration – design, implement, and iterate on multi‑agent systems that translate legacy logic into target‑state code (Kotlin/JVM). Build orchestration layers, tool‑use patterns, and guardrails that ensure output correctness for financial calculations.
• Build evaluation and verification infrastructure – create automated test harnesses that compare migrated calculation outputs against legacy results. Implement parity testing frameworks, regression suites, and confidence scoring to gate production cutover decisions.
• Contribute to the standard calculation runtime – help build and extend the target platform that migrated calculations deploy into, ensuring deterministic, immutable, auditable execution.
• Partner with domain SMEs – embed with mainframe subject‑matter experts across Credit, Money Market & Mutual Funds, Statements & Tax, and IBOR to validate agent outputs, refine prompt strategies, and close knowledge gaps in specifications.
• Extend ETL and CDC pipelines for agent workflows – build and integrate event sourcing, CDC (change data capture), and data pipelines that support end‑to‑end migrated workflows, including upstream/downstream dependency mapping.
• Operate AI systems in production – own LLMOps for the toolchain: deployment, monitoring, cost management, latency optimization, token budget management, and incident response. Ensure reliability and compliance for 24/7 operation.
• Iterate rapidly and ship continuously – work in tight build‑measure‑learn cycles, prototype quickly, instrument everything, and make data‑driven decisions about agent architectures, model selection, and prompt strategies.
• Contribute to shared tooling and infrastructure – build reusable libraries, evaluation harnesses, prompt templates, and orchestration patterns that scale AI capabilities across all four core processing domains.
• Drive adoption and governance of approved AI‑assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes, while establishing measurable validation standards and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
• Apply knowledge of tools within the Software Development Life Cycle toolchain, including approved AI‑assisted development and automation capabilities, to improve the value realized by automation at scale.

Required Qualifications

• Formal training or certification in software engineering concepts and 5+ years of applied experience.
• Hands‑on experience building LLM‑based applications – agentic architectures, RAG pipelines, prompt engineering, and evaluation frameworks.
• Strong software engineering fundamentals: distributed systems, event‑driven architectures, API design, testing practices, and cloud platforms (AWS/EKS/ECS).
• Expert proficiency with AI‑assisted development tools (Claude Code, GitHub Copilot, Cursor) as core daily workflow.
• Strong experience with Python development in production environments.
• Demonstrated ability to operate and debug complex systems – you own what you ship.
• Clear communicator who can articulate technical trade‑offs to both engineers and business stakeholders.
• Demonstrated experience leading effective use of enterprise‑authorized AI‑assisted software development tools within the work environment, with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
• Experience in Computer Science, Computer Engineering, Mathematics, or a related technical field.

Preferred Qualifications

• Experience with legacy systems, mainframe technologies (COBOL, JCL, DB2), or large‑scale migration programs.
• Familiarity with workflow orchestration (Temporal, Airflow) and event sourcing/CDC patterns, and experience building code analysis, translation, or verification tooling.
• Experience with Kafka, PostgreSQL, and container orchestration (Kubernetes/EKS).
• Background in financial services – wealth management, brokerage, or capital markets processing.

Equal Opportunity Statement

JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy, or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.