Python

JPMorganChase•Plano, TX

About The Position

As a Senior Lead Software Engineer at JPMorganChase within Consumer & Community Banking, Digital Technology, 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. You will work as an integral member of an agile team to build and deliver technology products. The role involves designing, developing, and troubleshooting complex software solutions; creating secure, high-quality production code; and producing architecture and design artifacts for complex applications. You will use data insights to improve applications, coding hygiene, and system architecture, contribute to software engineering communities, and adopt new technologies that can improve agentic solutions. You will collaborate with product, design, and operations teams and ensure software complies with security, privacy, and regulatory requirements. You will also lead team adoption of enterprise-authorized AI-assisted engineering practices and tools, establishing standards to validate AI-generated work through secure coding, peer review, and automated testing. The goal is to improve code quality, delivery speed, and operational outcomes while promoting reusable patterns.

Requirements

Formal training or certification in software engineering concepts.
At least 5 years of applied software engineering experience, including at least 2 years building complex scalable applications or agentic systems.
Hands-on experience building agentic systems using LLMs or SLMs.
Experience setting up and maintaining MCP servers and building MCP-compatible tools or adapters.
Proficiency in Java and/or Python.
Experience building production services with Spring AI or the Spring ecosystem (Spring Boot, Spring Security, Spring Cloud), or with Python frameworks such as FastAPI or Flask, including typed contracts, testing, and packaging.
Solid AWS background and working knowledge of ECS or EKS, Docker, and CI/CD tools such as GitHub Actions, Jenkins, or CodeBuild.
Strong API design skills, including REST and OpenAPI; gRPC familiarity is also specified.
Familiarity with observability stacks such as Splunk, CloudWatch, Prometheus/Grafana, or OpenTelemetry.
Practical understanding of LLM patterns, including function calling and tools, RAG, prompt management, context windows, token budgeting, and safety guardrails.
Strong testing practices, including unit and integration tests, load tests, and evaluation datasets for agents.
Demonstrated experience leading effective use of approved AI-assisted software development tools for coding, code review, test acceleration, or troubleshooting, and setting expectations for validating outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and adherence to resiliency and security expectations.
Experience coaching engineers on safe, compliant adoption of AI-assisted practices in delivery workflows.

Nice To Haves

Expertise with distributed orchestration patterns for LLM applications, including graph-based flows, retries, fallbacks, and guardrails, and secure integration with enterprise tools and data.
Experience with safe rollout strategies such as shadowing, A/B testing, progressive exposure, human-in-the-loop review, continuous quality and safety evaluation, and 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.

Responsibilities

Execute software design, development, and technical troubleshooting, using approaches suited to complex problems.
Create secure, high-quality production code and maintain algorithms that run synchronously with appropriate systems.
Produce architecture and design artifacts for complex applications and ensure software development meets design constraints.
Gather, analyze, synthesize, and visualize large, diverse data sets to support continuous improvement of software applications and systems.
Identify hidden problems and patterns in data and use insights to improve coding hygiene and system architecture.
Contribute to software engineering communities of practice and events exploring emerging technologies.
Adopt new technologies that can positively impact agentic solutions.
Collaborate with product, design, operations, and other cross-functional teams to deliver end-to-end solutions.
Ensure software solutions comply with security, privacy, and regulatory requirements.
Drive team adoption of enterprise-authorized AI-assisted engineering practices, including code review and refactoring, test acceleration, and incident or root-cause analysis support.
Establish consistent validation standards for AI-assisted work, including secure coding, peer review, and automated testing, and promote reuse of effective patterns.
Use Software Development Life Cycle tools, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized from automation.