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Salary
≈ $122k – $241k per year (Estimated)
Location
In office (Plano)
Seniority
Middle · 3+ 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 20, 2026. First seen by Alion on Oct 20, 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 Software Engineer at JPMorganChase within the Commercial & Investment Bank - Digital & Platform Services, 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.

  • Lead end-to-end delivery of full-stack features spanning ReactJS UI, Java/Python backend services, SQL persistence, and Kafka event streaming.

  • Drive spec-driven development by authoring, reviewing, and maintaining executable/validated specifications (APIs, events, data contracts) as the source of truth; ensure implementation and tests remain aligned to the spec.

  • Use AI-assisted development frameworks (e.g., OpenSpec, Spec-Kit, or similar) to accelerate requirements-to-spec, spec-to-implementation, and spec-to-test workflows while maintaining engineering rigor.

  • Design and implement scalable, resilient REST APIs, including clear interface contracts and backward compatibility strategies. Design and implement AWS-based components leveraging S3 and AWS Lambda where appropriate.

  • Build and operate containerized services using Docker and Kubernetes (deployments, services/ingress, configuration/secrets, health checks, autoscaling). Implement event-driven workflows using Kafka (topic strategy, consumer groups, retry/DLQ patterns, schema evolution).

  • Provision and manage environments using Terraform (repeatable infrastructure, safe change management). Build LLM-based implementations (e.g., RAG, tool/function calling) with appropriate evaluation, monitoring, and controls.

  • Establish SDLC standards: design reviews, code reviews, automated testing, CI/CD, documentation, and operational readiness. Mentor and coach engineers; provide technical direction and promote continuous improvement.

Required qualifications, capabilities, and skills

  • Formal training or certification on software engineering concepts and 3+ 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

  • Strong backend development experience with Python (e.g., FastAPI/Flask), including API and service development.

  • Strong frontend development experience with ReactJS (modern React patterns, TypeScript/JavaScript).

  • Hands-on experience with Docker and Kubernetes running production workloads. Experience with Kafka (producer/consumer patterns, error handling, schema evolution).

  • Strong SQL skills and relational data modeling experience; query optimization and indexing.

  • Hands-on AWS experience including S3 and AWS Lambda; cloud security fundamentals (e.g., IAM-based access patterns). Experience using Terraform for infrastructure-as-code.

  • Spec-driven development experience, including: Defining and maintaining machine-readable specs (e.g., OpenAPI for REST and/or AsyncAPI for event interfaces); Driving implementation and automated tests from specs (contract testing, validation, backward compatibility checks); Using AI-assisted spec-to-code / spec-to-test tooling (e.g., OpenSpec, Spec-Kit, or similar).

  • LLM (or other AI Tool) application experience in production (prompt/versioning practices, grounding/RAG concepts, evaluation/monitoring basics, secure data handling).

Preferred qualifications, capabilities, and skills

  • Experience working with relational databases such as DB2 or PostgreSQL.

  • 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.

  • Financial services knowledge, with an understanding of the commercial lending space.

  • Experience working in regulated environments requiring strong risk management, controls, and auditability.

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