Lead Software Engineer - Machine Learning

Job Description

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 and Investment Bank Payments Technology team, 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. You are a core technical contributor responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

• Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors.
• Deploy and serve end‑to‑end ML models.
• Deploy and maintain services in a fully cloud native environment.
• Drive 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.
• Develop secure and high‑quality production code, and review and debug code written by others.
• Drive decisions that influence the product design, application functionality, and technical operations and processes.
• Serve as a function‑wide subject‑matter expert in one or more areas of focus.
• Actively contribute to the engineering community as an advocate of firm‑wide frameworks, tools, and practices of the Software Development Life Cycle.
• Apply 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.
• Influence peers and project decision‑makers to consider the use and application of leading‑edge technologies.
• Add to the team culture of diversity, opportunity, inclusion, and respect.

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Hands‑on practical experience delivering system design, application development, testing, and operational stability.
• Advanced proficiency in one or more programming language(s).
• Advanced knowledge of software applications and technical processes with considerable in‑depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence, machine learning, mobile, etc.).
• 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.
• Ability to tackle design and functionality problems independently with little to no oversight.
• Practical cloud native experience.
• Experience in Computer Science, Computer Engineering, Mathematics, or a related technical field.

• Proficient in Python programming for building traditional ML models and APIs.
• Good understanding of designing and deploying OpenAPI compliant API services.
• Good understanding of building agentic workloads using AI frameworks such as Google ADK and LLamaIndex.
• Ability to configure OAuth2 based authentication and authorization flows for applications.
• Good understanding of Open Telemetry and modern microservice deployment patterns.
• Good understanding of AWS services and ability to deploy high‑reliability cloud native workloads.
• Knowledgeable in building and debugging ML models built using supervised and unsupervised learning algorithms.

J.P.…