As a Lead Software Engineer at JPMorganChase within the Consumer & community Banking, 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. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
Job responsibilities
- Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
- Develops secure and high-quality production code, and reviews and debugs code written by others
- 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.
- Drives decisions that influence the product design, application functionality, and technical operations and processes
- Serves as a function-wide subject matter expert in one or more areas of focus
-
Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Excellent experience in Java, platform engineering, AWS, systems & solutions design, proficiency matching to frameworks/cloud design patterns developed/ test harness development, SQL/NoSQL database design, migrations. Thought leadership in engineering and engineering operations excellence in SRE & Observability, high level design, and Low-level design reviews, code reviews, test case reviews messaging systems expertise, live application/data migrations, high scale, and production support from product and platform organizations. Migration of services, APIs, systems, databases in live production. Data and metrics based results and outcomes.
- AI agents creations, Skills, RAG with MCP, Context engineering, Automated generation of full stack/backend java code/APIs, designs, code, database, test cases, and assembling solutions in real time shortening the SDLC to significantly lesser timelines and lesser workforce capacity, using prompt engineering with LLM tools such as Gemini, Claude, CoPilot and assembling solution to result in production grade services/APIs using coding and performance best practices.
- Platform engineering experience with production support of microservices focused on key performance metrics, Self-Service feature creation, Observability, and SRE experience. Secure Development life cycle, complete service ownership in DevOps model. CI/CD tooling, Static and Dynamic Code review tools usage such as SonarQube, CheckMarx. Java 17+ Spring, Spring boot, Spring Cloud microservices.
- QA/Test automation writing automated unit tests, functional tests, and end‑to‑end test automation various testing tools and frameworks such as PlayWright, Selenium, java and RPA tools. Metrics based product feature delivery for functional and non-functional requirements.
- Kafka, Rest APIs, Splunk, Dynatrace, strong SQL, NoSQL Database such as Cassandra, React based front end UI development.
- Thought leadership, Insist on High Standards, Simplify and Invent, Do the Right Things, Disagree and Commit, Earn Trust, Bias for action, Customer focus, Learn and curiosity, Mentoring, Deep Dive, Ownership, Deliver Results, Positive mindset/attitude, team working, consensus building, excellent written and oral communication skills.
- Leading small teams, but influencing broader divisions with tech assets created, owning estimations & timelines.
- 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.
- Practical cloud native experience