JP Morgan Chase

Runs a consumer bank and an investment bank

Lead Software Engineer - Multiple Teams

AI Application Engineering +1Updated on Nov 7

Full-timePlano, TX, USANo salary listedIn-person

About the Job

Requirements

Formal training or certification in Software Engineering and at least five years of applied experience.
Strong system design, application development, and operational stability skills in production environments.
Hands-on experience using enterprise-authorized AI-assisted software development tools for coding, test creation, troubleshooting, or documentation, with the ability to evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
Ability to influence product design, application functionality, and technical operations within the team and domain by proposing pragmatic architectures, tradeoffs, and standards aligned with firm SDLC, security, and controls expectations.
Hands-on experience with large language models and generative AI use cases, such as retrieval-augmented generation, agents, prompt/tool orchestration, evaluation, and guardrails.
Experience with distributed systems and at least one major cloud platform: AWS, GCP, or Azure.
Expertise in microservices, RESTful APIs, and relational and/or NoSQL data technologies.
Practical experience building cloud-native systems, including event-driven architectures, streaming, or service mesh.
Familiarity with DevOps practices and tools for continuous integration and deployment.

Responsibilities

Design and develop creative full-stack software solutions using innovative approaches.
Build and implement AI-driven capabilities, including large-language-model-based services, orchestration, and integrations into business workflows.
Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, including AI-assisted code review and refactoring, test strategy acceleration, and incident/root-cause analysis support; establish validation standards such as secure coding, peer review, and automated testing, and promote reuse of effective patterns across the team.
Use enterprise-authorized AI coding-assistance tools to improve code quality, delivery speed, and productivity across complex deliverables, including code generation and refactoring, unit test creation, and documentation; validate outputs through peer review, automated testing, and secure coding standards, and contribute learnings and reusable patterns to improve broader team effectiveness.
Apply knowledge of Software Development Life Cycle toolchain tools, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Architect and deliver cloud-native microservices and APIs, including REST and streaming, ensuring scalability, resilience, and strong security controls.
Identify and automate solutions for recurring operational issues to improve system stability and observability through logs, metrics, and tracing.
Communicate project status clearly and manage priorities across multiple initiatives.
Collaborate within a Scrum team, participate in Agile ceremonies, and support a culture of diversity, opportunity, and inclusion.
Code in Java, AWS ECS, EKS, and PostgreSQL.
Use AI agents, including Copilot and Claude, to improve quality and delivery timelines.

Desired Qualifications

Cloud certification in AWS, GCP, or Azure.
Working knowledge of Python for AI/ML-based implementations.
Experience with multi-region service deployments and zero-downtime deployment.
Familiarity with Docker, Kubernetes, Helm, and modern CI/CD practices.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity and secure handling of inputs and outputs.
Track record of delivering scalable, reliable, and secure products from concept to launch.
Advanced Java proficiency as the primary language, plus working knowledge of Python for AI/ML integrations.

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 JPMorganChase within the Commercial & 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. 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:

Design and develop creative full-stack software solutions using innovative approaches.
Build and implement AI-driven capabilities, including LLM-based services, orchestration, and integrations into business workflows.
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.
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
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.
Architect and deliver cloud-native microservices and APIs (REST/streaming), ensuring scalability, resilience, and strong security controls.
Identify and automate solutions for recurring operational issues to improve system stability and observability (logs/metrics/tracing).
Communicate project status clearly and manage priorities across multiple initiatives.
Collaborate within a Scrum team, participate in Agile ceremonies, and support a culture of diversity, opportunity, and inclusion.
Codes in Java, AWS ECS, EKS, and Postgres
Utilizes AI agents (CoPilot, Claude) to improve quality and delivery timelines

Required qualifications, capabilities, and skills:

Formal training or certification in Software Engineering and 5+ years applied experience
Strong system design, application development, and operational stability skills in production environments.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
Influence product design, application functionality, and technical operations within the team and domain by proposing pragmatic architectures, tradeoffs, and standards aligned to firm SDLC, security, and controls expectations.
Hands-on experience with Large Language Models (LLMs) and generative AI use cases (e.g., RAG, agents, prompt/tool orchestration, evaluation/guardrails).
Experience with distributed systems and at least one major cloud platform (AWS, GCP, or Azure).
Expertise in microservices, RESTful APIs, and data technologies (relational and/or NoSQL).
Practical experience building cloud-native systems (event-driven architectures, streaming, service mesh, etc.).
Familiarity with DevOps practices and tools for continuous integration and deployment.

Preferred qualifications, capabilities, and skills:

Cloud certification in AWS, GCP, or Azure.
Working knowledge of Python (for AI/ML based implementations) a plus
Experience with multi region service deployments and zero downtime deployment
Familiarity with Docker, Kubernetes, Helm, and modern CI/CD practices.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs
Track record delivering scalable, reliable, and secure products from concept to launch.
Advanced Java proficiency (primary), plus working knowledge of Python (for AI/ML integrations) a plus.

About the Company

JP Morgan Chase

Runs a consumer bank and an investment bank

JPMorgan Chase runs the largest bank in the United States, which serves households and small businesses as Chase and large companies, governments, and investors as J.P. Morgan. During the 2008 financial crisis it bought the failing investment bank Bear Stearns and lender Washington Mutual, and in 2023 it took over First Republic Bank after regulators seized it. Chase takes deposits and earns interest on credit cards, mortgages, and car loans, while the investment side earns fees for advising on mergers, raising money for companies, trading stocks and bonds, and managing wealth. Formed in a 2000 merger, JPMorgan Chase traces its roots to 1799 and is based in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1799

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