Sr Director of Software Engineering - AI Governance

Shape how AI is governed, engineered, and scaled across JPMorganChase. This role sits at the intersection of policy, controls, and automation, turning responsible AI requirements into repeatable, auditable, and highly automated AI workflows. As a Senior Director of Software Engineering at JPMorganChase within the Chief Data and Analytics Office (CDAO), you will set and lead the AI governance framework enabling responsible adoption of AI/ML and generative AI at scale. You will partner with Risk, Legal, Compliance, Controls, and Technology to translate policy into enforceable processes, control designs, and scalable platforms, embedding governance directly into MDLC/SDLC via automation, evidence capture, and continuous reporting. You will drive executive transparency and ensure sustained readiness for audits, regulatory examinations, and client due diligence. Job responsibilities - Define and drive adoption of the in-business analytics ownership operating model, including roles, responsibilities, RACI/decision rights, and escalation paths across stakeholders. - Own and continuously improve AI/ML governance artifacts (procedures, charters, operating models, job aids), including versioning, periodic reviews, and controlled refresh cycles. - Partner with data science, architecture, engineering, and data teams to assess impacts of new policies/standards and convert requirements into actionable implementation plans, technical controls, and rollout playbooks. - Lead end-to-end execution of CDAO and firmwide AI governance rollout plans, including communications, milestones, dependencies, adoption KPIs, and exception management. - Design and build executive-ready governance platforms and reporting that provide real-time visibility into adherence, risk themes, control effectiveness, and remediation progress (with defensible audit trails and evidence-on-demand). - Advance governance automation through agentic workflow orchestration and SDLC/MDLC integration to minimize manual intervention and increase standardization, repeatability, and traceability. - Partner with Controls Management to identify, document, and monitor AI/ML risks, issues, and actions through established governance forums, including remediation tracking and control attestations. - Define and execute the strategic roadmap for AI governance tooling and systems by standardizing processes and embedding governance-as-code patterns (e.g., automated gates, policy checks, and evidence capture) into delivery pipelines. - Facilitate working sessions and stakeholder forums to drive alignment, resolve blockers, and promote consistent best practices across lines of business; serve as a senior interface to executives to drive decisions across competing 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. Required qualifications, capabilities, and skills - Formal training or certification on software engineering concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise . - Experience delivering or governing AI/ML and generative AI solutions in financial services or a highly regulated environment, including audit/regulatory readiness. - Proven ability to build agentic systems that automate legacy and complex systems to minimize manual intervention. - Experience leading large and/or cross-functional teams of technologists across multiple platforms and delivery streams. - Demonstrated experience influencing across highly matrixed organizations and delivering measurable outcomes at scale (adoption, cycle time reduction, risk reduction, cost-to-serve). - 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 initiatives spanning system design, automated testing, CI/CD, and operational stability (e.g., reliability engineering, incident/problem management integration). - Demonstrated cloud-native engineering experience (e.g., containerization, orchestration, infrastructure-as-code, runtime governance, and secure-by-default patterns). - Ability to define and operationalize governance metrics and telemetry (e.g., control coverage, SLA/SLO-aligned reporting, exception trends, remediation aging) for executives and regulators. - Experience hiring, developing, and recognizing talent; building high-performing teams with strong engineering and control ownership. Preferred qualifications, capabilities, and skills - Strong understanding of microservices architectures, cloud systems, and large-scale data platforms. - Familiarity with model risk management concepts and governance expectations for machine learning models. - Knowledge of banking, markets, and trading, and how analytics is applied across those domains. - Experience building executive reporting routines, governance metrics, and dashboards that support control outcomes. - Exposure to governance tooling implementation, workflow design, controls automation, and evidence automation initiatives.