Lead Software Engineer - Python Full Stack, AI/ML

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 Commercial & Investment Bank, 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

• Executes creative software solutions, design, development, and technical troubleshooting, thinking beyond routine or conventional approaches to build solutions or break down technical problems.
• Delivers end-to-end solutions in the form of cloud-native, microservices-based applications, leveraging the latest technologies and best industry practices.
• Uses domain modeling techniques to build best-in-class business products, structuring software for clarity, testability, and evolution.
• Develops secure high-quality production code, and reviews and debugs code written by others.
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
• Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies • Adds to team culture of diversity, opportunity, inclusion, and respect. Investigates and resolve issues, ensuring they do not resurface.
• Designs and build solutions that avoid single points of failure using scalable architectural patterns. Designs and build scalable, secure, and reliable solutions by leveraging modern architectural patterns that ensure zero-downtime releases and optimize data performance.
• 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

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Proficiency in back-end technologies (e.g., Python, Flask, Django) with experience building microservices-based applications;  for full-stack roles, proficiency also includes front-end technologies (e.g., HTML, CSS, JavaScript, Typescript, React, Angular).
• Well versed with AI/ML technologies (e.g. LLM, AI Python libraries, LLM tools, ML foundations)
• Demonstrated ability to independently solve design and functionality challenges with minimal supervision.
• Experience working with cloud platforms (e.g., AWS, Azure, GCP), distributed systems, and web technologies, including RESTful APIs and web services, WebSockets, and JSON.
• Hands-on experience designing and building scalable applications using SQL and NoSQL databases. Experience with agile development methodologies (e.g., Scrum) and an understanding of the software development life cycle.
• Understanding of application resiliency. Proficiency in automation and continuous delivery methods.
• Proficient in all aspects of the Software Development Life Cycle. Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.
• 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

• Strong soft skills, including p…