Director of Software Engineering - Regulatory Capital, Data and Experience

JPMorgan Chase JPMorgan Chase · Banking · Bengaluru, Karnataka, India · Corporate Sector

Director of Software Engineering at JPMorgan Chase leading adoption of agentic AI-enabled engineering practices and SDLC/TLM automation within a technical area, focusing on improving speed, quality, and operational outcomes while establishing guardrails for validation, security, resiliency, and reuse. Requires experience in leading these practices, understanding responsible AI use, and hands-on experience in financial regulatory environments.

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. Provide visionary leadership and strategic direction for development and delivery of products, applications, and technologies
  4. Serve as a hands-on engineering leader, guiding design patterns, coding standards, architecture decisions, and platform best practices
  5. Translate complex technical challenges and emerging trends into actionable insights for senior leadership

Skills

Required

  • Formal training or certification on software engineering concepts and 10+ years applied experience
  • Tenured technologist operating within Financial and Technology domains for 15+ years
  • 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.
  • Directly manage and provide high-level direction to teams working on cloud-native solutions, microservices, and user interface experiences
  • Deep understanding of software engineering best practices, system design, testing, and operational stability
  • Hands-on experience in Basel implementations, regulatory transformations, and advanced technologies like Gen AI

Nice to have

  • Experience with Databricks and other cloud-native platforms
  • Familiarity with agile methodologies
  • Knowledge of emerging industry trends

What the JD emphasized

  • agentic AI-enabled engineering practices
  • responsible AI use
  • safe scaling patterns

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
  • safe scaling patterns