If you are a software engineering leader ready to take the reins and drive impact, we’ve got an opportunity just for you.
As a Director of Software Engineering (Executive Director) at JPMorgan Chase as a part of the Dublin Database Cross Product Engineering Team, you will lead the creation of a new AI-first engineering capability for the Database product line.
The role requires a senior engineering leader who can operate as the function’s go-to subject matter expert, influence senior stakeholders across business, product, and technology, and advise cross-functional teams on complex technology decisions. You will establish durable, reusable software frameworks, technical methods, and AI-enabled delivery practices that scale across teams and functions.
You will also provide leadership for the AI-enabled pilot model, ensuring that human-plus-AI engineering delivery remains secure, controlled, auditable, production ready, and measurable against productivity, quality, operational stability, and cross-product adoption outcomes.
Job responsibilities
- Create complex, scalable, and reusable coding frameworks using appropriate software design frameworks, enabling durable capabilities that can be leveraged across Database teams, products, and functions.
- Develop secure, high-quality production code and provide senior review, debugging, technical challenge, and quality oversight for code written by others.
- Serve as the function’s go-to subject matter expert for Database cross-product engineering, advising cross-functional teams on architecture, engineering practices, product delivery, controls, and technology decisions within the domain.
- Contribute to the development of technical methods in specialized fields, aligned to modern product development methodologies, cloud-native engineering, AI-first delivery, secure SDLC, and enterprise control requirements.
- Lead the design and delivery of reusable software frameworks, shared components, automation, APIs, SDKs, infrastructure-as-code, control-plane capabilities, and engineering accelerators adopted across multiple teams.
- Influence leaders and senior stakeholders across business, Product, and Technology teams to align the cross-product engineering backlog, technical strategy, delivery priorities, and adoption roadmap.
- Guide AI-enabled engineering delivery by defining disciplined human-plus-AI workflows, validation checkpoints, auditability, guardrails, escalation paths, productivity measures, and quality controls.
- Mentor senior engineers and emerging talent, strengthening engineering excellence, reusable design patterns, technical decision-making, and adoption of AI-first engineering practices across the Dublin capability.
- 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.
Required qualifications, capabilities, and skills
- Formal training or certification in software engineering concepts and significant applied experience delivering complex, secure, high-quality production software.
- Hands-on practical experience across system design, application development, testing, operational stability, production readiness, secure SDLC, and enterprise-scale engineering delivery.
- Expertise in one or more programming languages, with advanced knowledge of software application development, technical processes, and at least one technical discipline such as cloud, artificial intelligence, machine learning, mobile, platform engineering, or database engineering.
- Experience applying deep technical expertise and new methods to determine solutions for complex technology problems in one or more specialized technical disciplines.
- Experience leading a product as a Product Owner or Product Manager, including backlog shaping, stakeholder alignment, adoption planning, measurable outcomes, and delivery governance.
- Ability to present, influence, and communicate effectively with senior leaders and executives across business, Product, and Technology.
- Strong understanding of the business, including how engineering decisions affect risk, controls, cost, resiliency, operational performance, customer outcomes, and strategic delivery.
- Practical cloud-native experience, including modern application architecture, automation, resiliency, observability, and scalable platform or control-plane delivery.
- 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.
Preferred qualifications, capabilities, and skills
- Experience leading cross-product platform engineering, database engineering, cloud database services, database-as-a-service models, or enterprise control-plane capabilities.
- Experience establishing reusable frameworks, engineering standards, technical methods, and adoption patterns across multiple teams, products, or functions.
- Experience building or governing AI-enabled engineering workflows, including AI agents, automated testing, documentation generation, anomaly detection, operational diagnostics, or developer productivity tooling.
The successful candidate will be expected to lead as an AI-first engineering executive. This means setting the operating model for how AI agents and AI-assisted engineering practices are embedded into secure, controlled, auditable, and production-ready delivery. The role requires senior accountability for guardrails, quality controls, validation patterns, escalation boundaries, reusable context, productivity measures, and adoption across teams. The Executive Director of Software Engineering should be comfortable using AI to accelerate analysis, architecture, design, code generation, test creation, documentation, controls evidence, operational diagnostics, and reusable framework development, while ensuring that human engineering judgment, security, resiliency, controls compliance, and business outcomes remain the final accountability.