Sr Lead Software Engineer - Java / Python

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products. As a Senior Lead Software Engineer at JPMorganChase within the Corporate Sector - Infrastructure Platforms, 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. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications. Job responsibilities Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors Develops secure and high-quality production code, and reviews and debugs code written by others Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain. Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale. Drives decisions that influence the product design, application functionality, and technical operations and processes Serves as a function-wide subject matter expert in one or more areas of focus Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle Required qualifications, capabilities, and skills 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 Deep expertise in designing, developing, and modernizing large-scale enterprise applications with a strong focus on scalability, resiliency, security, performance, and maintainability. Strong hands-on development experience across multiple programming languages, including Java and Python , with the ability to rapidly learn, evaluate, and adopt emerging technologies. Proven expertise in modern software architecture, including microservices, distributed systems, API-first design, event-driven architecture, cloud-native development, domain-driven design, and enterprise integration patterns. Strong understanding and practical experience with AI-driven software engineering , including AI-assisted development tools, GenAI applications, AI agents, Agentic AI frameworks, prompt engineering, and Model Context Protocol (MCP) based integrations and development. Ability to establish and drive engineering best practices including secure SDLC, architecture governance, CI/CD, automated testing, observability, code quality standards, and responsible adoption of AI technologies with appropriate validation, security, and governance controls. Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls. Preferred qualifications, capabilities, and skills Experience building modern UI and full-stack applications using React, Node.js, responsive design principles, and reusable component-based architectures. Experience with cloud platforms and services, particularly AWS (EKS, EC2, S3, Lambda, IAM), Kubernetes, containers, and cloud-native engineering practices. Experience designing and implementing AI-powered solutions, agent orchestration frameworks, MCP-enabled integrations, RAG architectures, and enterprise-scale automation platforms. Passion for continuous learning, technology modernization, mentoring engineers, and driving innovation through AI-enabled engineering practices.