Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.
As a Senior Lead Software Engineer at JPMorganChase within, Consumer & Community Banking – Deposits 2.0 team, you are an integral part of an agile team that enhances, builds, and delivers secure, stable, and scalable technology products. You will drive meaningful business outcomes by applying deep technical expertise, strong engineering judgment, and a customer-focused mindset to solve complex problems across distributed systems, cloud, and artificial intelligence.
Job responsibilities
- Lead end-to-end design and delivery of scalable Java services and APIs, optimizing for performance, resiliency, and maintainability in production environments
- Architect and implement cloud-native solutions on Amazon Web Services, applying well-structured patterns for reliability, observability, and cost-aware scalability
- Mentor engineers through thoughtful code reviews, design guidance, and pragmatic engineering standards that raise quality and accelerate delivery
- Collaborate with product, design, and data partners to define technical strategies that improve user experience and automate key workflows
- Build and optimize data pipelines using Databricks and Apache Spark to enable analytics and machine learning workflows at scale
- Drive engineering excellence across continuous integration and delivery, automated testing, and secure software development practices
- Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Advanced Java experience, including building Spring Boot–based services in a microservices architecture
- Experience with Java (Core & EE, Spring Boot, Spring MVC, Spring Cloud)
- Practical experience delivering system design, application development, automated testing, and operational stability for production systems
- Hands-on experience building cloud-native applications on Amazon Web Services (e.g., compute, storage, database, container, and serverless services)
- Experience with continuous integration and delivery and modern build/version control practices (e.g., Git, Maven/Gradle, and pipeline automation)
- Proficiency with automated testing approaches and frameworks (e.g., JUnit and mocking frameworks) and a strong quality-first mindset
- Experience with relational databases and SQL, including data modeling and performance considerations for high-throughput systems
- Knowledge of messaging and integration patterns, including event streaming technologies such as Kafka
- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.
Preferred qualifications, capabilities, and skills
- Advanced Python experience for automation, data engineering, or machine learning enablement
- Experience building and deploying agentic or AI-assisted workflows, including evaluation and human-in-the-loop validation patterns
- Experience with infrastructure as code and cloud provisioning automation (e.g., Terraform, CloudFormation, or AWS CDK)
- Experience with containerization and orchestration (e.g., Docker and Kubernetes) for scalable service operations
- Experience with CockroachDB and Go (Golang) in distributed system environments