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 Payments - Kinexys team, 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
Lead an engineering team to deliver assigned scope, or operate as a senior individual contributor depending on program needs.
Own end-to-end solution design and implementation for microservices and APIs using Java and Spring Boot. Produce clear High-Level Design (HLD) and Low-Level Design (LLD) documents; ensure traceability from requirements to implementation.
Collaborate with product, architecture, and operations to plan releases, manage dependencies, and de-risk delivery. Contribute to cloud-ready architecture and deployment patterns; partner on AWS/Azure implementations where applicable.
Drive continuous improvement in process, tooling, and platform capabilities; address production issues with a focus on resiliency. Guide and mentor junior engineers: code reviews, technical coaching, best practices.
- 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. Adds to team culture of diversity, opportunity, inclusion, and respect.
- 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.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Expert proficiency in Java and Spring Boot and familiar with tools like Intellij and Visual code. Using AI tools for daily development and simplifying day to day developer lifecycle like copilot/claude.
- Building RESTful services, microservices, and API-first designs. Dependency injection, configuration management, modular design
- Strong experience designing complex distributed systems and documenting HLD/LLD. Performance tuning, concurrency, memory management
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Proficiency in automation and continuous delivery methods and in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
- In-depth knowledge of the financial services industry and their IT systems. Practical cloud native experience.
- 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
- Banking/payments domain knowledge (e.g., authorization, clearing/settlement, transaction processing, reconciliation, ISO 20022 standards ,Different payment channels like Swift/pain/MX).
- Cloud knowledge on AWS or Azure (compute, storage, networking, IAM/security); familiarity with containerization (Docker) and orchestration (Kubernetes) is a plus.
- Kotlin experience or knowledge; ability to read/write Kotlin and understand Java interoperability.
- Observability practices (metrics, logs, tracing) and SRE principles for production systems.
- Experience with messaging/event streaming (e.g., Kafka), caching, and resiliency patterns (circuit breakers, retries, timeouts).
