Lead Software Engineer

JPMorgan Chase & Co.•San Francisco, CA

12d•$152,000 - $215,000

About The Position

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorganChase within the Infrastructure Platforms Digital Workflows Product Line, you will lead delivery of high-quality technical solutions, drive roadmaps and agile execution, mentor engineers, and implement AI-enabled engineering capabilities (including tool functions/skills and MCP servers/agents) within governance, security, and controls.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Strong proficiency in Java and strong working experience with Python.
Strong architecture and design skills across systems with sound engineering judgment.
Strong engineering fundamentals (OOD, design patterns, SOLID) and a quality-first mindset (testing/TDD).
Strong integration and data fundamentals (SQL/PL-SQL; event messaging such as MQ/Kafka).
Experience operating production services with CI/CD, observability, and reliability best practices.
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) 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 engineers on safe, compliant adoption within delivery practice.
Ability to create roadmaps/milestones, manage dependencies, and execute effectively in agile/scrum environments.
Track record of mentoring engineers, leading reviews, and raising engineering standards across a team.
Strong communication and collaboration skills across distributed, cross-functional stakeholders.

Nice To Haves

ServiceNow experience (including familiarity with the ServiceNow data model).
Experience using AI-assisted development tools responsibly with strong engineering judgment and secure coding practices.
Experience with MCP operations patterns (authN/Z, logging/traceability, rate limiting, versioning, safe rollout).
Experience designing AI agents with grounding/RAG patterns, deterministic fallbacks, prompt/version management, and eval harnesses.
Understanding of data privacy/security considerations for agent workflows (data minimization, secrets management, least privilege, auditability).
Experience in AWS or other public cloud platforms.

Responsibilities

Lead end-to-end design, implementation, and operation of complex platform features and integrations.
Create technical roadmaps and milestones; drive predictable delivery and continuous team process improvement.
Develop assistant skills/tool functions with clear interfaces, schemas, validation, error handling, and observability.
Stand up and operate MCP (Model Context Protocol) servers/integrations and agent patterns to accelerate delivery.
Establish evaluation checks, guardrails, and monitoring so AI behavior is accurate, safe, and verifiable.
Drive data readiness and integration practices (modeling, pipelines alignment, quality controls) underpinning AI outcomes.
Uphold SDLC rigor: secure coding, code reviews, automated testing, CI/CD, and production readiness.
Mentor junior engineers (technical and soft skills), delegate effectively, and unblock delivery through technical leadership.
Partner with product and analyst stakeholders on estimates, delivery plans, dependencies, and outcomes.
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.