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.
Job Responsibilities
- 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
Required Qualifications, Capabilities, and Skills
- 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
Preferred Qualifications, Capabilities, and Skills
- 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.