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Salary
≈ $143k – $277k per year (Estimated)
Location
In office (Plano)
Seniority
Staff · 5+ years exp
Visa
H-1B filings in 12 months: 3,429 · for this role: 1,686 · green card filings: 933

Confirmed on the employer's own hiring board on Oct 10, 2026. First seen by Alion on Sep 14, 2026. JPMorganChase scores A on the Alion truth index.

Overview
Company
Impact
Profile match
JPMorganChase is the largest bank in the United States by assets and one of the most systemically important financial institutions in the world, with a lineage running back through more than a thousand predecessor firms to the 1799 founding of the Bank of the Manhattan Company. It combines a dominant investment bank and markets business with Chase, the largest retail banking franchise in America, plus commercial banking and asset and wealth management. Headquartered in New York, the group is unusual among banks for the scale of its technology spending, running one of the largest engineering organisations of any financial institution and deploying its own internal AI platform across the firm.

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 Corporate Sector - Infrastructure Platforms, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

  • Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems

  • Develops secure and high-quality production code, and reviews and debugs code written by others

  • 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.

  • Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture

  • Lead the design and implementation of platform services and infrastructure with an emphasis on architecture, integration patterns, and system reliability

  • Develop, review, debug, and maintain secure, high-quality production code, applying engineering judgment to ensure it meets our standards regardless of whether it originates from you, a teammate, or an approved artificial intelligence tool

  • Identify opportunities to eliminate or automate remediation of recurring issues to improve operational stability across applications and systems

  • Lead design reviews and architecture discussions to evaluate solutions, strengthen technical quality, and support scalable delivery

  • Partner with platform architecture, trust and authorization teams, and downstream IAM consumers to land coherent designs across organizational seams; Lead communities of practice across Software Engineering to increase awareness and adoption of new and leading-edge technologies

  • Mentoring and coaching engineers through technical guidance

Required qualifications, capabilities, and skills

  • Formal training or certification on software engineering concepts and 5+ years applied experience

  • Hands-on practical experience delivering system design, application development, testing, and operational stability

  • 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 practices

  • Experience with Identity and Access Management platforms, role-based access control, or entitlement governance at scale

  • Advanced proficiency in one or more programming languages, frameworks, or platforms such as Java, Spring, Go, Python, Terraform, or Kubernetes

  • Practical cloud-native experience building and operating services on at least one public cloud provider, such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform

  • Advanced understanding of agile methodologies such as continuous integration and continuous delivery, application resiliency, and security

  • Ability to evaluate technical solutions with a customer-focused mindset and understand how engineering decisions impact users and business outcomes

  • Strong communication skills with the ability to adapt messages for technical and non-technical audiences

  • Responsible use of approved artificial intelligence tooling, with critical judgement to validate quality, correctness, security, and performance and experience with Terraform

Preferred qualifications, capabilities, and skills

  • Experience working in financial services or another highly regulated technology environment with formal audit and control posture

  • Experience contributing to public cloud platform services, developer platforms, or shared engineering capabilities

  • Familiarity with responsible artificial intelligence use in engineering workflows, including secure handling of inputs and outputs

  • Experience improving developer experience, platform reliability, or operational efficiency at scale

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