JPMorgan Chase—Director of Software Engineering - Java/Python, AI

Job description

Description: As a Director of Software Engineering in Autonomous Infrastructure and AI4Reliability team you will lead a small, senior team designing, building, and operating tools and services which will manage and heal our infrastructure using AI and agentic flows. You will be a hands-on technical leader, owning production systems end-to-end, setting architecture and engineering standards, and fostering the growth of your team. You’ll work AI-native across the software development lifecycle, ensuring correctness, security, reliability, and cost-effectiveness. Job Responsibilities: - Lead and grow a small team of senior engineers, fostering a high-performance, high-ownership culture - Own the design, delivery, and operation of enterprise-scale infrastructure services from architecture through production - Write and review production code, maintaining a hands-on approach and setting the bar for engineering quality - Establish AI-native engineering practices with robust validation standards to ensure speed never compromises correctness - Participate in an on-call rotation and act as an escalation point for production incidents, building operability and observability from the start - Analyze and optimize systems for scalability, efficiency, reliability, and performance - Decompose ambiguous problems into clear, executable work for both engineers and AI agents - Sets direction and governance for agentic AI-enabled engineering and SDLC/TLM automation within a technical area to drive measurable improvements in speed, quality, and operational outcomes (e.g., AI-orchestrated delivery workflows, release readiness controls, automated test modernization, and incident triage acceleration), while establishing guardrails for validation, security, resiliency, traceability, and reuse across teams. - 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 and support capacity unlock initiatives at scale. - Define success criteria, measure impact, and hold the team accountable to outcomes - Partner with stakeholders across the infrastructure organization, resolving technical disagreements and proactively raising risks Required Qualifications, Capabilities, and Skills: - Hands-on software engineering background with production coding experience in an industry-standard language (e.g., Python, Go, Java, C++, Rust) - Experience running production systems at scale, including on-call ownership, incident response, and designing for reliability and operability - Experience leading or mentoring engineers and setting technical direction - Strong systems thinking, including interfaces, contracts, failure modes, and interactions at scale - Ability to direct AI tools for real engineering work, with sound judgment on where AI applies and where human expertise is required - Security-first mindset, integrating risk judgment from design through production - Clear, direct communication with engineers, stakeholders, and peers - Outcome orientation, focused on impact, reliability, and cost - Comfort operating with ambiguity and greenfield scope - Inclusive, collaborative leadership style, able to attract, grow, and retain strong senior engineers Preferred Qualifications, Capabilities, and Skills: - Track record of reducing operational toil and cost through automation and better engineering - Experience adopting AI-native engineering practices at team or organizational scale - Experience leading adoption of agentic AI-enabled engineering practices (using enterprise-authorized tools within the work environment) across teams, including defining operating expectations (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs. - Strong understanding of responsible AI use and control expectations in engineering workflows, including data sensitivity, resiliency/security implications, and governance; ability to influence leaders on safe scaling patterns and reuse. - Prior experience in regulated or large-scale enterprise environments in the payment industries - Experience with greenfield builds and establishing engineering culture