Lead Software Engineer, Java Microservices/API

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As a Lead Software Engineer at JPMorganChase within the Corporate Sector – Corporate Technology, 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 the design, development, and delivery of scalable, secure, cloud-native backend services and RESTful APIs using Java, Spring Boot, and microservice architecture .
- Provide technical leadership across the full software development lifecycle, including architecture, solution design, coding standards, implementation, testing, deployment, observability, and production support.
- 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.
- Drive engineering excellence by producing high-quality production code, performing rigorous code reviews, improving test coverage, and ensuring adherence to secure coding and organizational engineering standards; Design and implement resilient distributed systems that meet performance, scalability, availability, and maintainability requirements.
- Develop and deploy cloud-native services on AWS , leveraging services such as ECS , containerization with Docker , and automated CI/CD delivery practices.
- Design, optimize, and maintain data models, schemas, and queries in PostgreSQL , ensuring data integrity, performance, and scalability across distributed services.
- Produce and review architecture, technical design, and implementation artifacts for complex applications, ensuring alignment with enterprise architecture, security, and operational standards; Identify recurring technical or operational issues and lead automation, remediation, and resiliency improvements to reduce toil and improve application stability.
- Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, testing effectiveness, documentation, troubleshooting, and operational outcomes.
- Establish team standards for validating AI-generated outputs, including secure coding review, correctness checks, performance validation, automated testing, and responsible handling of data; Lead technical evaluation sessions with internal stakeholders, vendors, and external partners to assess architecture designs, integration approaches, technical feasibility, and alignment with existing systems.
- Analyze application telemetry, operational data, quality metrics, and delivery patterns to identify trends and drive continuous improvement in engineering practices and system reliability; Contribute to engineering communities of practice and support initiatives focused on modern software engineering, cloud-native development, automation, AI-assisted delivery, and emerging technologies.

Required qualifications, capabilities, and skills

- Formal training or certification on software engineering concepts and 5+ years applied experience
- Strong hands-on experience designing, developing, and maintaining enterprise-grade applications using Java and Spring Boot .
- Demonstrated experience building and supporting RESTful APIs , microservices, and distributed service-oriented systems; Practical cloud-native development experience, preferably with AWS , including ECS , containers, and cloud deployment patterns.
- 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
- Hands-on experience with Docker , containerized applications, and modern deployment practice; Strong understanding of CI/CD pipelines, automated testing frameworks, release automation, and DevOps engineering practices.
- Solid experience with PostgreSQL , including relational data modeling, query optimization, indexing strategies, and performance tuning; Advanced understanding of the software development lifecycle, agile methodologies, application resiliency, observability, and secure software delivery.
- Proven ability to lead technical design discussions, evaluate implementation options, and make sound engineering decisions for complex systems; Experience reviewing code, mentoring engineers, and promoting engineering best practices across a team.
- Demonstrated experience adopting or leading effective use of approved AI-assisted software development tools for coding, code review, test acceleration, documentation, refactoring, or troubleshooting.
- Strong understanding of responsible AI usage in engineering workflows, including validation of AI outputs, data sensitivity considerations, secure handling of inputs and outputs, and adherence to security and resiliency expectations.
- Strong analytical, debugging, and problem-solving skills with the ability to diagnose complex technical issues across applications, infrastructure, and data layers; Excellent communication and collaboration skills, with the ability to influence technical direction and partner effectively with cross-functional stakeholders.

Preferred qualifications, capabilities, and skills

- Experience with synchronous REST integration patterns for latency-sensitive credit/bureau calls, plus event-driven and asynchronous processing using Kafka, Amazon SQS, or similar — including dual-persistence patterns (e.g., Aurora PostgreSQL for transactional data and Snowflake for analytics and long-term audit).
- Familiarity with API gateway technologies, service mesh concepts, and IDAnywhere / ADFS OAuth2 authentication/authorization patterns, plus edge-proxy vendor integration patterns (ELB → ECS → Secrets Manager → edge proxy → vendor API) and API lifecycle management for third-party bureau integrations.
- Experience with infrastructure-as-code tools such as Terraform (Atlas / TFE), , or similar automation frameworks, and CI/CD delivery with blue/green deployments; Hands-on experience with observability tools, including logging, metrics, tracing, dashboards, and alerting platforms (Splunk, Dynatrace, CloudWatch, Grafana), and driving production readiness for regulated, high-availability services.
- Experience designing systems for high availability, fault tolerance, scalability, and operational resilience — including ECS NextGen cluster migrations, multi-AZ resiliency, and graceful degradation for downstream vendor outages; Familiarity with performance testing, load testing, and application tuning (BlazeMeter / JMeter) for high-throughput, low-latency distributed systems under real-time credit-decisioning SLAs.
- Experience with modern security practices, including secrets management (AWS Secrets Manager, Hera KMS, AppProtect), vulnerability remediation , dependency management, secure API design, and controls for 15-year audit retention and consumer credit data (soft-pull vs hard-pull semantics); Experience leading technical modernization, platform migration, and cloud adoption— and application resiliency initiatives across a regulated environment; Track record of contributing to engineering communities of practice, reusable frameworks, and technical standards , and mentoring engineers toward consistent, secure, and scalable delivery.