Senior Director of Software Engineering

JPMorgan Chase JPMorgan Chase · Banking · OH · Consumer & Community Banking

Senior Director of Software Engineering to lead multiple technical areas and departments, focusing on the adoption and implementation of agentic AI-enabled engineering and SDLC/TLM automation within the Marketing Technology group. The role involves setting strategy, establishing guardrails, and driving the adoption of AI-assisted development capabilities to improve speed, scalability, reliability, and cost-to-serve, with a strong emphasis on responsible AI risk and controls.

What you'd actually do

  1. Sets and scales 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; establishes guardrails for validation, security, resiliency, traceability, and reuse.
  2. Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive cross-domain reuse and measurable capacity unlock outcomes across departments.
  3. Provides leadership and high-level direction to teams while frequently overseeing employee populations across multiple platforms, divisions, and lines of business
  4. Acts as the primary interface with senior leaders, stakeholders, and executives, driving consensus across competing objectives
  5. Manages multiple stakeholders, complex projects, and large cross-product collaborations

Skills

Required

  • 10 + years of experience managing Software Delivery teams and enterprise products.
  • Experience developing or leading large or cross-functional teams of technologists
  • Demonstrated prior experience influencing across highly matrixed, complex organizations and delivering value at scale
  • Experience leading multi-organization adoption of agentic AI-enabled engineering operating models (using enterprise-authorized tools within the work environment), including defining 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 demonstrated ability to advise senior leaders on safe adoption, portfolio governance, and reuse-first strategies.
  • Experience leading complex projects supporting system design, testing, and operational stability
  • Experience with hiring, developing, and recognizing talent
  • Extensive practical cloud native experience

Nice to have

  • Advanced proficiency in managing full stack technical delivery teams and enterprise products.
  • Hands-on expertise as a Web or Full Stack Developer, IT Consultant, or related technical role.
  • Deep knowledge of big data processing frameworks (e.g., Spark, Flink, Storm) and stream processing with Kafka.
  • Strong understanding of data design, modeling principles, and architecture patterns (data lake, lakehouse, data mart, data fabric, data mesh).
  • Advanced experience with cloud services and cloud-native data technologies (AWS EMR, Glue, Lambda, MSK, RDS, DocumentDB; AWS preferred).
  • Expertise in semantic technologies, modeling, graph databases (Neo4J, Datastax Graph, AWS Neptune), and graph processing languages (Gremlin, Cypher, SparQL).
  • Advanced hands-on knowledge of web or middleware security solutions and server-side web technologies.

What the JD emphasized

  • agentic AI-enabled engineering
  • agentic AI-enabled engineering operating models
  • responsible AI risk, controls, and resiliency/security expectations at scale

Other signals

  • AI-enabled engineering
  • agentic AI-enabled engineering
  • AI-orchestrated delivery workflows
  • agentic AI-enabled engineering operating models