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Director of Software Engineering

JPMorgan Chase
Ohio, USA
Full-time
Director · 10+ years experience
Onsite
Discovered 4 days ago

Full Job Posting

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.
  • 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 scaling patterns and reuse.
  • Practical cloud native experience
  • Expertise in Computer Science, Computer Engineering, Mathematics, or a related technical field

Preferred qualifications, capabilities, and skills

  • Experience working at code level

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