Director of Software Engineering

JPMorgan Chase JPMorgan Chase · Banking · Hyderabad, Telangana, India · Consumer & Community Banking

Director of Software Engineering at JPMorgan Chase to lead a technical area and drive impact within teams, technologies, and projects. The role involves setting direction and governance for agentic AI-enabled engineering and SDLC/TLM automation, applying knowledge of AI-assisted development tools, and leading the adoption of agentic AI-enabled engineering practices. Requires strong understanding of responsible AI use and control expectations in engineering workflows.

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. Collaborate with product and engineering teams to deliver robust cloud-based solutions that drive enhanced customer experiences.
  4. Leads technology and process implementations to achieve functional technology objectives
  5. Makes decisions that influence teams’ resources, budget, tactical operations, and the execution and implementation of processes and procedures

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
  • Experience developing or leading cross-functional teams of technologists
  • 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.
  • Experience with hiring, developing, and recognizing talent
  • Experience leading multiple solutions
  • Practical cloud native experience
  • Expertise in Computer Science, Computer Engineering, Mathematics, or a related technical field
  • Experience influencing senior stakeholders and peer leaders across business product and technology teams
  • Experience using AI/ML to improve quality of software and solution
  • Experience with Big Data / Distributed / cloud technology

Nice to have

  • Effective communication and stakeholder management skills, especially in high-visibility transformation programs
  • Action-oriented, decisive, drives results systematically.
  • Master’s preferred.
  • Skilled at assessing risk and making decisions with a holistic, big-picture perspective.
  • Demonstrates a can-do attitude and leads by example.

What the JD emphasized

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

Other signals

  • agentic AI-enabled engineering
  • SDLC/TLM automation
  • AI-orchestrated delivery workflows
  • release readiness controls
  • automated test modernization
  • incident triage acceleration
  • responsible AI use
  • AI-assisted development