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 - Java/Spring Boot/AWS at JPMorgan Chase within the Corporate Technology's Consumer and Community Banking Risk Decision 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

• 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
• Hands-on experience leading system design, application development, testing, deployment, and production operational stability in mission‑critical environments
• Advanced proficiency in Java (Java 17+) with Spring Boot, including writing thread‑safe, highly concurrent code on the JVM
• Proven experience building cloud-native Java microservices and large-scale distributed systems (real-time and batch), with deep end-to-end SDLC expertise from design and development through release, monitoring, and support, and a strong command of CI/CD, automation, resiliency, and security to enable continuous delivery and production excellence.
• Extensive experience with event‑driven architectures and distributed messaging platforms such as Kafka
• Practical, hands‑on experience running production workloads on AWS, leveraging services such as EKS, ECS, S3, ALB/NLB, DynamoDB, and Aurora
• Advanced containerization and orchestration experience using Docker and Kubernetes in high‑availability production environments
• Proficiency with SQL and NoSQL databases (e.g., Oracle, PostgreSQL, DynamoDB) and performance tuning at scale
• Strong experience with infrastructure as code (Terraform) and observability tooling including Grafana, Splunk, Dynatrace, Datadog, and CloudWatch
• 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

• Experience with Databricks and large‑scale data processing platforms
• Exposure to mobile technologies and mobile application development ecosystems
• Hands‑on experience applying machine learning concepts in production systems
• Familiarity with advanced SQL and NoSQL databases such as CockroachDB and Cassandra
• Experience working with data‑intensive or analytics‑driven applications
• Knowledge of AI/ML frameworks or cloud‑native data services
• Background in modern data architectures supporting real‑time decisioning