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 JPMorgan Chase within the Consumer & Community Banking (CCB) division, you will design and build critical technology solutions across Chase AI components, including Chase Agent, Agent Operating Memory, Domain Agents, Agentic Experience Service, and Assurance.
- Executes software solutions, design, development, and technical troubleshooting, thinking beyond routine approaches to solve complex problems.
- Creates secure, high-quality production code and maintain algorithms that run synchronously with appropriate systems.
- Produces architecture and design artifacts for complex applications, ensuring design constraints are met by software code development.
- Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets to drive continuous improvement of software applications and systems.
- Proactively identifies hidden problems and patterns in data, using insights to drive improvements in coding hygiene and system architecture.
- Contributes to software engineering communities of practice and events that explore new and emerging technologies.
- Adopts and learn new technologies that positively impact agentic solutions.
- Collaborates with cross-functional teams, including product, design, and operations, to deliver end-to-end solutions.
- Ensures compliance with security, privacy, and regulatory requirements in all software solutions.
- 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
- 5+ years of software engineering experience, with 2+ years building complex scalable applications or agentic systems.
- Hands-on experience building agentic systems using LLMs/SLMs
- Experience setting up and maintaining MCP servers and building MCP-compatible tools/adapters
- Proficient in coding in one or more languages: Java and/or Python
- Proficiency building production services with either Spring AI or the Spring ecosystem (Spring Boot, Spring Security, Spring Cloud), or Python (FastAPI/Flask), with typed contracts, testing, and packaging.
- Solid AWS background with working knowledge of ECS or EKS, containerization (Docker), and CI/CD (GitHub Actions/Jenkins/CodeBuild).
- Strong API design skills (REST/OpenAPI; gRPC and familiarity with observability stacks (e.g., Splunk, CloudWatch, Prometheus/Grafana, OpenTelemetry).
- Practical understanding of LLM patterns: function calling/tools, RAG, prompt management, context windows, token budgeting, and safety guardrails.
- Strong testing culture: unit/integration tests, load tests, and evaluation datasets for agents.
- 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
- Expertise with distributed orchestration patterns for LLM applications (graph-based flows, retries, fallbacks, guardrails) and secure integration with enterprise tools and data.
- Experience with safe rollout strategies (shadowing, A/B testing, progressive exposure), human-in-the-loop review, and continuous evaluation for quality and safety, including canary rollouts.
- Knowledge of API gateways, service mesh, and multi-region high availability and disaster recovery for mission-critical services.
- Familiarity with data privacy, security best practices, and regulatory compliance in financial services.
- Experience with performance optimization, scalability, and reliability engineering for large-scale systems.
- Ability to evaluate and integrate third-party tools, libraries, and frameworks to accelerate development.
- Demonstrated leadership in technical communities, open source contributions, or industry forums.