Sr Lead Software Engineer — Python/ AWS/Databricks

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 Credit data pond team housed within Wholesale credit risk technology 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. Establishes an enterprise-aligned operating model for AI-assisted engineering across teams (approved use cases, guardrails, RACI, auditability), ensuring consistent adoption that improves throughput and quality without increasing risk. Defines and enforces measurable validation standards for AI-assisted changes (mandatory peer review, secure coding controls, automated test thresholds, performance/regression checks, release readiness criteria) and embeds them into CI/CD quality gates. 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. Sets the enterprise toolchain strategy and target-state architecture across planning, source control, build, test, security, deployment, and observability—driving standardization and reducing fragmentation across teams. Establishes “automation-at-scale” governance (golden pipelines, shared libraries/templates, reference implementations, and platform guardrails) to accelerate delivery while maintaining resiliency and security expectations. 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 advanced applied experience in Python Hands-on practical experience delivering system design, application development, testing, and operational stability Advanced in one or more programming language(s) Establishes a scalable operating model for enterprise-authorized AI-assisted development (approved use cases, guardrails, roles/approvals, auditability) and drives consistent adoption across multiple teams. Sets clear validation expectations for AI outputs before merge/release—mandatory peer review, secure coding checks, automated test coverage thresholds, and documented acceptance criteria for high-risk or customer-impacting changes. 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. Establishes and enforces responsible AI governance for engineering workflows (approved use cases, data classification rules, escalation paths, auditability), ensuring adoption aligns with enterprise policy and regulatory expectations. Defines secure handling standards for AI inputs/outputs—prohibiting sensitive data exposure, mandating redaction/minimization, controlling access via least privilege, and ensuring appropriate logging and retention practices. Ability to tackle design and functionality problems independently with little to no oversight Practical cloud native experience