Sr Director of Software Engineering - AI Governance
On-siteNew York City, New York, United States or New York, United States
Job Summary
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, converting requirements into actionable implementation plans, technical controls, and rollout playbooks. Lead end-to-end execution of firmwide AI governance rollout plans, design executive-ready governance platforms, and advance automation through agentic workflow orchestration to minimize manual intervention. Partner with Controls Management to monitor risks, issues, and actions through established governance forums, while defining the strategic roadmap for AI governance tooling and systems. Facilitate working sessions to drive alignment, resolve blockers, and promote consistent best practices across lines of business.
Required Qualifications
- Formal training or certification on software engineering concepts
- 10+ years applied experience
- 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
Desired Qualifications
- 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
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