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Salary
≈ $122k – $242k per year (Estimated)
Location
In office (Tampa)
Seniority
Staff · 5+ years exp

Confirmed on the employer's own hiring board on Oct 5, 2026. First seen by Alion on Aug 3, 2026. The Infatuation scores A on the Alion truth index.

Overview
Company
Impact
Profile match
The Infatuation is a restaurant discovery and review publication headquartered in New York, publishing guides, ratings and recommendations on where to eat in cities across the US, the UK and beyond. Founded in 2009, it has been owned by JPMorgan Chase since 2021 and runs editorial teams in cities such as New York and London, with its content also tied to Chase dining benefits. Its own hiring centres on staff writers, editors, video and social producers, audience and partnerships staff, and product and engineering roles for its guides and app.

Be an integral part of an agile team that’s constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As an AWS Lead Software Engineer-Full Stack/Java/SpringBoot at JPMorgan Chase within Commercial and Investment Banking's Payments Technology team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading healthcare technology product 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 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
  • Adds to team culture of diversity, opportunity, inclusion, and respect

Required qualifications, capabilities, and skills

  • Formal training or certification on Software Engineering concepts and 5+ years applied experience
  • Hands-on experience coding in Java, Spring Boot, JPA, SQL, REST
  • Experience with event driven architecture using Kafka
  • Experience building RESTful backend applications using Spring Boot and solid relational enterprise databases with Oracle
  • Experience with version control systems, such as Git.
  • Strong knowledge of CI/CD e.g. Jenkins
  • Hands-on practical experience in system design, application development, testing and operational stability
  • Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
  • 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
  • Solid understanding of cross cutting concerns such as Application Resiliency, and Security

Preferred qualifications, capabilities, and skills

  • Familiarity with Groovy and Gradle
  • Experience coding in Python
  • Strong understanding of containerization technologies such as Kubernetes or Docker
  • Exposure to AI/ML technologies - especially familiarity with model training and inference
  • Exposure to AWS cloud technologies
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