Director of Software Engineering - Head of Platform and Software Engineering, Branch Technology

hackajob is collaborating with J.P. Morgan to connect them with exceptional professionals for this role.

JOB DESCRIPTION

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 at JPMorgan

Chase within the Corporate Sector - Employee Platforms team, you lead a technical area and drive impact within teams, technologies, and projects across departments. Utilize your in-depth knowledge of software, applications, technical processes, and product management to drive multiple complex projects and initiatives, while serving as a primary decision maker for your teams and be a driver of innovation and solution delivery.

The Head of Platform and Software Engineering for Branch Technology owns the strategy and delivery for the software-defined platforms underpinning critical Retail Branch systems, most significantly the Software Defined Branch (SDB) platform, which serves as the control and management plane for the branch technology ecosystem ding a small, senior engineering team, this leader will drive the continued evolution of SDB, build the next-generation Retail Event Management platform aligned to our Next Gen Workspace strategy, and manage the near-term transition of several applications to business partners.

Beyond delivery, central to this role is a commitment to AI-first engineering. This organization has moved past the question of whether to embed AI into the development lifecycle, that decision is made.

The right candidate brings hands-on experience building AI-native systems and the leadership to make that the team's default way of working. AI is how a lean, senior team delivers at the scale this role demands.
Job responsibilities

• Leads technology and process implementations to achieve functional technology objectives
• Accountable for decisions that influence teams' resources, budget, tactical operations, and the execution and implementation of processes and procedures
• 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.
• Drive the continued development of the Software Defined Branch platform.
• Build the next-generation Retail Event Management system.
• Operate with precision at enterprise scale.
• Carries governance accountability for coding decisions, control obligations, and measures of success such as cost of ownership, maintainability, and portfolio operations
• Delivers technical solutions that can be leveraged across multiple businesses and domains
• Influences peer leaders and senior stakeholders across the business, product, and technology teams

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
• Experience developing or leading cross-functional teams of technologists
• Experience with hiring, developing, and recognizing talent
• 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…