at J.P. Morgan
Bulge Bracket Investment BanksPosted 13 days ago
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**Senior Lead Software Engineer - AI** Experienced software engineer sought for agile team enhancing AI-driven tech solutions at JPMorganChase. Lead team, build secure production code, drive AI-assisted practices, and deliver scalable solutions. Key responsibilities include technical guidance, DevOps, pattern design, and collaboration across domains. Must have 5+ years of software experience, cloud native skills, and proficient AI-assisted tool usage. Proven mentorship capabilities essential.
- Compensation
- Not specified
- City
- Plano
- Country
- United States
Currency: Not specified
Full Job Description
Location: Plano, TX, United States
As a Senior Lead Software Engineer at JPMorganChase within the Corporate Technology - Engineering and Architecture team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
You will deliver reusable, production-grade software that advances Corporate Technology outcomes across GenAI adoption, innovation-to-production conversion, and governance transparency. This role is accountable for building scalable solutions that reduce duplicate effort, accelerate cycle time, and deliver finance-validatable capacity shift.
- Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
- Develops secure and high-quality production code, and reviews and debugs code written by others
- Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Applies 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.
- Build and operate secure, resilient services that support discovery, reuse, and delivery at enterprise scale.
- Convert high-potential ideas into production deployments through stage-gated engineering execution.
- Embed governance, auditability, and controls into the full software lifecycle.
- Design for reuse before rebuild by publishing and maintaining shared patterns, APIs, and implementation templates.
- Partner across architecture, security, SRE, testing, product, and data to deliver measurable business outcomes.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Lead technical design and implementation for platform services and workflow integrations.
- Build capabilities that improve discoverability of existing assets and ownership to reduce parallel delivery.
- Deliver automation that improves engineering speed, first-pass quality, and operational reliability.
- Implement telemetry for adoption, reuse, cycle time, quality, and value realization.
- Create production readiness evidence for every rollout: resiliency, security, rollback, observability, and runbook completeness.
- Support innovation funnel execution from intake through feasibility, acceleration, and production.
- Mentor engineers and raise team standards in coding, testing, and operational excellence.
- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.
- Ability to tackle design and functionality problems independently with little to no oversight
Practical cloud native experience
