Senior Lead Software Engineer - AI/ML Developer

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Senior Lead Software Engineer at JPMorgan Chase within the Consumer & Community Banking's AI/Machine Learning Platform Engineering Team, you serve as a seasoned member of an agile team focused on building, scaling, and maintaining robust machine learning platforms. You will design and deliver trusted, market-leading infrastructure and tools that empower data scientists and ML engineers to develop, deploy, and monitor models efficiently and securely. You are responsible for implementing critical technology solutions across multiple technical areas to support the firm’s business objectives and drive innovation in ML platform capabilities.

Job responsibilities

• Designs, builds, and maintains scalable machine learning platforms and infrastructure to support end-to-end ML workflows.

• Develops and optimizes tools for model training, deployment, monitoring, and lifecycle management.

• Integrates data engineering, feature management, and model serving capabilities into unified ML platform solutions.

• Implements secure, high-quality production code for platform services, APIs, and automation pipelines.

• Leads evaluation sessions with data scientists, ML engineers, and product teams to understand requirements and deliver platform features that accelerate ML development and operations.

• Ensures platform reliability, scalability, and performance through proactive monitoring, troubleshooting, and continuous improvement.

• Produces architecture and design artifacts for platform components, ensuring alignment with enterprise standards and best practices.

• Automates infrastructure provisioning, configuration, and CI/CD pipelines for ML platform services.

• Contributes to the ML platform engineering community of practice and participate in events that explore new and emerging technologies

• 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