Senior Director of Software Engineering (Infrastructure)

• Lead multiple technology and process implementations across departments to achieve firmwide technology objectives.
• Directly manage multiple areas with strategic transactional focus, balancing speed, risk, resiliency, and cost-to-serve outcomes.
• Set and scale multi-department strategy for agentic AI-enabled engineering and SDLC/TLM automation (using enterprise-authorized tools within the work environment) to drive firmwide objectives (speed, scalability, reliability, and cost-to-serve), including portfolio-level standards for AI-orchestrated delivery workflows, release governance, automated test modernization, resilience engineering, and incident response acceleration; establish guardrails for validation, security, resiliency, traceability, and reuse.
• Drive cloud-native platform modernization across teams, including Kubernetes-based application/platform enablement, standardized deployment patterns, and production readiness practices.
• Lead infrastructure-as-code adoption and portfolio standards using Terraform, enabling repeatable, compliant provisioning across environments.
• Define and enforce multi-region cloud architecture patterns (active/active and active/passive where appropriate) to improve availability, resiliency, and recovery objectives.
• Apply deep knowledge of the SDLC toolchain including enterprise-authorized AI-assisted development, CI/CD automation, and release controls to increase engineering throughput and reliability.
• Partner with security and controls to strengthen cloud identity and access practices, including AWS IAM design, least-privilege access, and traceable approvals for sensitive actions.
• Establish resilience engineering programs including failure-mode testing and game days; accelerate incident response through automation and standardized runbooks (including chaos engineering tooling such as Gremlin where authorized).
• Provide leadership and high-level direction to teams while overseeing employee populations across multiple platforms, divisions, and lines of business.
• Act as the primary interface with senior leaders, stakeholders, and executives, driving consensus across competing objectives and large cross-product collaborations.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 10+ years applied experience; 5+ years leading technologists to solve complex technical problems within your domain.
• Experience developing and/or leading large and cross-functional teams of technologists, including hiring, developing, and recognizing talent.
• Demonstrated ability to influence across highly matrixed organizations and deliver value at scale through standards, platforms, and operating model change.
• Experience leading multi-organization adoption of agentic AI-enabled engineering operating models (using enterprise-authorized tools within the work environment), including governance (human-in-the-loop decisioning, quality gates), measurement frameworks, and secure handling of sensitive inputs/outputs across teams.
• Deep understanding of responsible AI risk, controls, and resiliency/security expectations at scale, with the ability to advise senior leaders on safe adoption, portfolio governance, and reuse-first strategies.
• Extensive practical cloud-native experience, including building and operating services on AWS.
• Strong experience with Kubernetes (designing scalable workloads, platform enablement, operational patterns, and reliability practices in production).
• Strong experience with infrastructure-as-code, especially Terraform, including modularization, policy/guardrails, and environment standardization.
• Experience designing and operating multi-region cloud architectures, including HA/DR patterns, observability, and reliability targets (RTO/RPO).
• Experience implementing secure identity and access controls in the cloud, including IAM concepts (least privilege, role-based access, and auditability).
• Experience modernizing delivery pipelines and operational excellence practices, including CI/CD, automated testing, and resilience/chaos engineering approaches (e.g., Gremlin).