Leads a senior software engineering team building and operating enterprise-scale infrastructure services using AI and agentic workflows. Owns architecture, production systems, reliability, security, observability, cost optimization, and incident response. Sets engineering standards and governance for AI-enabled software development, establishes validation and security guardrails, writes and reviews production code, mentors engineers, and partners with infrastructure stakeholders to deliver measurable operational outcomes.
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
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