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JPMorgan Chase & Co. in Hyderabad is seeking a Senior Director of Software Engineering within Operations Technology to lead multiple technical domains and drive strategic platform development.
You will champion best practices, deliver scalable, resilient, and user-centric solutions, and guide adoption of advanced technologies across the firm. The role requires directing large-scale programs, mentoring delivery leads, and collaborating with executives to ensure on-time, on-budget delivery.
Shape the future of financial services at JPMorganChase. Join us to drive innovation, deliver impactful solutions, and lead teams at the forefront of technology.
As Senior Director of Software Engineering at JPMorgan Chase within Operations Technology, you will lead multiple technical domains, managing high-performing teams and collaborating across the organization. You will drive strategic platform development, champion industry best practices, and ensure delivery of scalable, resilient, and user-centric solutions. Your leadership will guide the adoption of advanced technologies and frameworks, aligning with the firm’s vision and objectives.
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.
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.
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.