JPMorganChase
Website:
jpmorganchase.com
Company:
https://www.linkedin.com/company/jpmorganchase
Seniority: Not Applicable
Industries: Financial Services
Job details:
Job Description
Join a high-performing agile team where your expertise shapes modern, user-centric software products. Unlock career growth by leveraging advanced AI, Java, and Python full-stack development skills.
Job Summary
As a Lead Software Engineer at JPMorgan Chase within the Employee Platforms Team, you design, build, and maintain end-to-end applications, including backend services, APIs, and UI, while embedding AI capabilities using strong prompt engineering practices. You apply deep expertise in software engineering, application architecture, and secure development, continuously expanding your knowledge across the product and AI landscape. You collaborate with cross-functional teams to deliver impactful solutions and foster a culture of innovation.
Job Responsibilities
- Design, develop, test, and deliver Java- or Python-based backend services and full-stack web applications for business-critical platforms
- Develop responsive and accessible frontend experiences (e.g., React/Angular) and integrate them with backend APIs, with experience in Kafka or other messaging tools
- Own a workstream or project across the full SDLC, including requirements, design, implementation, code review, and production support
- Build and maintain REST/GraphQL APIs, integrate with internal services, and ensure reliability, performance, and observability at the application level
- Design and develop next generation applications on Public Cloud infrastructure
- Implement secure coding practices, data handling controls, and automated testing (unit, integration, end-to-end)
- Partner with product, UX, data, and QA teams to deliver high-quality outcomes and drive continuous improvement
- Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, while establishing consistent validation standards and promoting reuse of effective patterns
- Apply 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
- Consider upstream/downstream data and system implications within the application domain and propose mitigations
Required Qualifications, Capabilities And Skills
- Formal training or certification on software engineering concepts and 10+ years applied experience in software development
- Strong proficiency in Java (e.g., Java 21+) or Python, Django, Flask, Spring/Spring Boot, building APIs, and service-oriented/microservice architectures
- Strong full-stack experience with modern JavaScript/TypeScript and a framework such as React or Angular
- Experience with databases (SQL and/or NoSQL), data modeling, and query optimization from an application perspective
- Experience with CI/CD usage, code quality practices, automated testing, and production support within application teams
- Solid understanding of application security (OAuth/OIDC, secure API design, secrets handling, OWASP)
- Prompt engineering skill: prompt patterns, structured prompting, tool/function calling concepts, output validation, and iterative refinement
- Strong communication and stakeholder management skills, with ability to influence across technical and business teams
- Demonstrated experience leading effective use of approved AI-assisted software development tools 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
Preferred Qualifications, Capabilities And Skills
- Experience building AI-enabled products (RAG patterns, embeddings, vector stores, evaluation harnesses)
- Experience with event-driven systems (Kafka or similar) and distributed system design
- Familiarity with containers and orchestration concepts (developer-level usage)
- Experience mentoring engineers, leading design reviews, and setting coding standards
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