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Lead Software Engineer - Python / AI - Athena Core - Vice President
lead · Technology / Software Development
Join us to shape the future of developer experience on one of the firm’s most strategic technology platforms. You’ll have the opportunity to impact how software is built across a vast engineering organization, tackling meaningful technical challenges and driving innovation. We value product thinking, technical ownership, and a passion for building tools that developers love. Here, your ideas will be adopted at scale, and your work will be visible across the organization. If you’re excited to make a difference and grow your career, this is the place for you.
As a Lead Software Engineer at JPMorgan Chase in the Athena Core DevTools team, you will design and build systems that accelerate and improve the daily work of thousands of engineers. You will shape the engineering experience from code creation to production release, partnering with teams across technology, platform engineering, and governance. You’ll work on high-impact projects that enhance developer productivity, quality, and controls. Our team values collaboration, innovation, and a focus on delivering tools that make a real difference. You will be part of a culture that encourages ownership and continuous improvement.
Job Responsibilities:
- Build and evolve developer-facing products, including IDE experiences, web tooling, test infrastructure, and SDLC workflows
- Improve productivity and confidence for thousands of engineers through impactful tooling
- 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.
- Own features throughout their lifecycle: discovery, design, implementation, rollout, telemetry, and operational support
- Translate ambiguous challenges into scalable platform capabilities
- Partner with controls and audit stakeholders to implement effective, low-friction engineering controls
- Influence engineering standards and best practices across a broad developer community
- Design and implement IDE and editor capabilities for smarter navigation and code intelligence
- Drive AI adoption in local development tools to enhance software development practices
- Develop static analysis and auto-remediation tools to prevent errors before production
- Build test frameworks and scheduling systems for large-scale workloads, including cloud-based execution
- Create platform tooling to identify code duplication, dead code, and opportunities for codebase simplification
Required Qualifications, Capabilities, and Skills:
- Strong software engineering fundamentals and passion for developer tooling
- Proficiency in multiple programming languages, with emphasis on Python; familiarity with TypeScript/React and SQL for full stack development
- 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.
- Solid understanding of testing, reliability, and maintainable system design
- Ability to operate independently in ambiguous problem spaces and collaborate effectively across teams
- Skill in turning loosely defined requirements into robust, widely adopted solutions
- Working knowledge of modern engineering workflows, including testing, CI/CD, static analysis, version control, and deployment
Preferred Qualifications, Capabilities, and Skills:
- Experience building developer tools or platforms at scale
- Familiarity with large-scale Python codebases
- Exposure to AI-driven development tools and practices
- Experience with cloud-based test execution and infrastructure
- Knowledge of regulatory or audit requirements in engineering environments
- Background in platform migrations or large-scale software initiatives
- Experience influencing engineering standards and best practices