Director of Software Engineering

JPMorgan Chase JPMorgan Chase · Banking · Plano, TX +1 · Consumer & Community Banking

Director of Software Engineering for the Deposits team, leading technology and process implementations. Focuses on setting direction and governance for agentic AI-enabled engineering and SDLC/TLM automation, including AI-orchestrated delivery workflows, release readiness controls, automated test modernization, and incident triage acceleration. Also responsible for reliability engineering, observability standards, and influencing stakeholders.

What you'd actually do

  1. 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.
  2. 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.
  3. Sets direction for reliability engineering practices within a technical area, including SLO/SLI definition, error budgets, capacity planning, and resilient design patterns aligned to business outcomes.
  4. Establishes observability standards across services (metrics, logs, traces, dashboards, alerting) to accelerate detection, triage, and remediation while reducing toil and repeat incidents.
  5. Drives end-to-end engineering decisions by assessing downstream impacts across integrated platforms, data flows, and dependent systems, and aligning changes to enterprise resiliency and risk expectations.

Skills

Required

  • Formal training or certification on software engineering concepts and 10+ years applied experience
  • 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
  • Strong understanding of end-to-end system behavior and downstream impacts, including dependency management, data integrity considerations, back-pressure/capacity constraints, and failure-mode analysis across integrated services.
  • 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

What the JD emphasized

  • agentic AI-enabled engineering
  • responsible AI use
  • agentic AI-enabled engineering practices

Other signals

  • AI-orchestrated delivery workflows
  • AI-enabled engineering
  • agentic AI-enabled engineering practices
  • responsible AI use