Director of Software Engineering

Are you ready to build something new from the ground up? Join us as we launch our Dublin engineering hub, where you’ll set the technical culture and drive innovation in enterprise infrastructure. You’ll lead a high-impact team, collaborate with talented peers, and influence engineering practices across JPMorganChase. Here, your leadership and technical expertise will help shape the future of our platforms. If you’re passionate about building and want your work to scale, this is your opportunity.

As a Director of Software Engineering in Infrastructure Platforms within our Dublin engineering hub, you will lead a small, senior team designing, building, and operating enterprise-scale infrastructure platforms. 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. You’ll be matched to a platform domain that fits your expertise and help establish our engineering culture.

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)
• Deep experience building and operating systems in at least one infrastructure domain (cloud, networking, compute, storage, security, or data infrastructure)
• 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:

• Experience across multiple infrastructure domains or programming languages
• 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
• Experience with greenfield builds and establishing engineering culture