Cloud & AI Software Lead — AWS & GCP

JP Morgan Chase

Jersey City (NJ)

In loco

USD 140.000 - 190.000

Tempo pieno

9 giorni fa

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Descrizione del lavoro

JPMorgan Chase in Jersey City is looking for a Lead Software Engineer to join a high-performing agile team. You will architect, build, and operationalize trusted, scalable software products across multiple domains, with a focus on secure, production-ready code.

You will lead design reviews, mentor engineers, and promote AI-assisted engineering practices to improve quality and delivery speed. The role demands deep Java expertise, cloud experience, and a passion for innovative, enterprise-grade

Competenze

Formal training or certification in software engineering with 5+ years practical experience.
Hands-on experience delivering system design, application development, testing, and operational stability.
Experience with Java development.
Knowledge of cloud skills and/or hands-on experience with AWS or GCP.
Understanding of Agile methodologies such as CI/CD, Applicant Resiliency, and Security.
Familiar with Linux/UNIX environment.
Experience working with AI agents for engineering.

Mansioni

Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
Develops secure high-quality production code, and reviews and debugs code written by others
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
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
Leads communities of practice across Software Engineering to drive awareness and use of new and leading‑edge technologies
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.