Senior Director of Software Engineering at JPMorganChase in London
- Company: JPMorganChase
- Location: LONDON, United Kingdom
- Job type: full time
- Workplace: onsite
- Posted: 2026-10-13
All open roles at JPMorganChase
Job description
Join us to shape the future of trading technology at JPMorganChase. As a leader, you will drive impactful solutions for the Global Commodities Exotics Desk, collaborating with top talent and business partners. You’ll have the opportunity to influence strategy, deliver high-value outcomes, and foster a culture of innovation and excellence. Your work will empower traders and transform the way we deliver technology. Be part of a team where your leadership and vision make a difference. As a Senior Director of Engineering in the Commercial & Investment Bank (CIB) Markets Technology team, you will lead the engineering organization responsible for front-office, desk-critical workflow and trading technology. You will set the vision and drive execution for end-to-end stack delivery, supporting the full trade lifecycle from execution to reporting. You will partner with Trading leadership, business management, Quants/Strats, and Functional leads to shape the technology roadmap and ensure production readiness. You will build and mentor a high-performing team, fostering a culture of ownership and excellence. Your leadership will directly impact the desk’s success and innovation. Job Responsibilities: Own the technology strategy for desk workflow and tooling, translating business priorities into actionable roadmaps. Partner with Traders, Desk Heads, Functional leads, Quants/Strats, and Operations to identify pain points and deliver value. Represent the desk technology organization in senior forums, driving alignment and securing commitments. Oversee end-to-end delivery across the SDLC, ensuring predictable execution and front-office responsiveness. Establish delivery rhythms and drive a production-first culture focused on observability, performance, and operational controls. Lead the design and delivery of integrated workflows, ensuring consistency and traceability across the trade lifecycle. Coordinate cross-team delivery for clean, aligned releases with strong change management. Set and enforce engineering standards for architecture, secure coding, testing, CI/CD, and incident response. 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. Sponsor and lead resolution of complex production incidents and systemic issues. Required Qualifications, Capabilities, and Skills: Deep expertise in software engineering, system design, testing strategy, operability, secure engineering, and performance. Experience in Commodities Front Office Technology, supporting Exotics/Commodities quant desks and trader workflows. Significant experience leading engineering teams and delivering end-to-end stack systems in production at scale. Demonstrated success partnering with Front Office/Trading stakeholders in high-urgency environments. Strong technical depth in Python (preferred) or ability to lead in a Python-based stack. 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 demonstr
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