Lead Software Engineer Python GenAI

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As an Lead Software Engineer Python GenAI at JPMorgan Chase within the Corporate Sector's Model Delivery and Platform Engineering 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. 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

• Designs, codes, tests, and delivers automation (including LLMs/agents) to eliminate manual operational work and streamline AO workstreams’ remediations (e.g., control items, security vulnerabilities, upgrades, and FARM findings).

• Governs application risk, controls, and compliance: owns adherence to firm standards, partners with Technology Risk & Controls, manages Technology Lifecycle Management (TLM), and drives closure of issues/findings (e.g., FARM) through effective remediation and evidence management.

• Owns security and data accountability for the application: ensures strong authentication/authorization, vulnerability and certificate hygiene, and proper data registration/classification plus compliant storage, retention, and disposal.

• Coordinates across product and engineering at scale to prioritize and drive execution with a clear sense of urgency for AO workstreams, including influencing/coaching teams and aligning execution across large developer communities.

• Runs resilient, well-operated production services end-to-end: implements monitoring/logging and anomaly detection, maintains secure network configurations/least privilege, and leads/supports incident, problem, and change management and recovery/resiliency readiness.

• Demonstrates and champions site reliability culture and practices and exerts technical influence throughout the team.

• Leads initiatives to improve the reliability and stability of applications and platforms using data-driven analytics to improve service levels.

• Collaborates with team members to identify comprehensive service level indicators and stakeholders to establish reasonable service level objectives and error budgets with customers.

• Documents and shares knowledge within the organization via internal forums and communities of practice.

• 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.