Role SummaryThe Director, AI & Emerging Technology Risk provides strategic leadership across client delivery, pre-sales, and advisory services related to artificial intelligence and emerging technologies. This role blends
AI expertise, enterprise risk management, technology risk leadership, solution architecture, and software development oversight to help clients innovate responsibly while managing risk. The Director plays a key role in shaping go-to-market strategy, supporting pre-sales efforts, and serving as an executive advisor throughout the client lifecycle.
Artificial Intelligence & Emerging Technology Leadership- Advise executive stakeholders on AI and emerging-technology risk across governance, ethics, security, privacy, and operational resilience-grounded in how models are built, deployed, and monitored in production
- Lead client decisioning on GenAI/ML adoption by translating business use cases into implementable architectures, control requirements, and delivery roadmaps (people/process/technology)
- Track evolving model capabilities, platforms, and threat/regulatory trends to continuously refine solution patterns, controls, and sales positioning for risk-focused AI offerings
Enterprise Risk & Technology Risk Strategy- Design and operationalize AI/technology risk frameworks aligned to ERM-mapping controls to the AI/ML lifecycle (data, training, evaluation, deployment, monitoring, change management)
- Assess enterprise-wide AI impacts across cybersecurity, privacy, model risk management (MRM), third-party risk, and compliance-producing actionable remediation plans and control implementation backlogs
- Present AI risk posture, risk appetite alignment, and control maturity to Boards and executive committees using clear metrics (KRIs/KPIs), model inventories, and audit-ready documentation
Solutions Architecture & Software Oversight- Architect AI-enabled solutions for risk consulting clients (including GenAI/RAG patterns) with secure-by-design, scalable, and governable reference architectures
- Evaluate SDLC and MLOps practices-cloud landing zones, APIs, data pipelines, CI/CD, model registries, and deployment strategies-to identify risk, control gaps, and engineering fixes
- Embed "risk-by-design" into build plans: threat modeling, privacy engineering, evaluation/validation protocols, human-in-the-loop controls, monitoring/alerting, and incident response runbooks
Client Delivery & Practice Leadership- Lead AI risk and emerging-technology engagements from strategy to implementation-owning delivery plans, sprint execution, stakeholder management, and outcomes
- Establish delivery governance (scope, RAID, quality gates, model validation checkpoints) and ensure solutions are production-ready, auditable, and defensible
- Build and mentor cross-functional teams (risk, data science, engineering, security) and provide technical leadership across Managers and Senior Managers
- Develop reusable accelerators (reference architectures, control libraries, evaluation harnesses, templates) and publish market-facing thought leadership to scale delivery and sales
- Drive account expansion by identifying adjacent risk opportunities, quantifying value, and partnering with client leaders to turn delivery success into follow-on sales
Go-to-Market Leadership- Own delivery sales for AI risk pursuits: qualify opportunities, shape deal strategy, and lead solutioning as the senior AI engineering/risk SME
- Define and package AI risk solution offerings (e.g., GenAI governance, MRM uplift, secure RAG, AI SDLC/MLOps controls) with clear value propositions and deliverables
- Lead proposal development-scoping, staffing model, delivery approach, assumptions, pricing inputs, and risk-managed implementation plan
- Run client workshops and executive presentations that walk through architectures, control designs, and delivery plans-bridging engineering details to risk outcomes
- Translate transformation goals into implementable AI risk solutions aligned to regulatory guidance, internal governance, and target-state technology architecture
- Build trusted C-level relationships and maintain pipeline by connecting delivery success to measurable risk reduction, audit readiness, and operational resilience
Experience, Skills & Qualifications- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Risk, or related discipline (or equivalent practical experience)
- 8-10+ years leading technology risk and/or AI engineering delivery in consulting or industry, including ownership of large client programs
- Job relevant certification (ie, Azure, Aws, AI certifications, etc.)
Preferred Qualifications- Deep knowledge of modern AI/ML and GenAI (LLMs, RAG, evaluation, safety), and the ability to translate that into concrete risk controls and implementation patterns
- Proven delivery sales experience: shaping pursuits, leading solutioning, contributing to pricing, and presenting to executive buyers
- Strong understanding of cloud and data architecture, SDLC, and MLOps/LLMOps (CI/CD, IaC, observability, model registry, feature stores, policy-as-code)
- Demonstrated ability to embed governance, security, privacy, and model risk management early in design/build, and to stand up audit-ready evidence and controls
- Executive-level communicator who can translate between engineers, risk leaders, regulators/auditors, and business stakeholders; comfortable owning both delivery and commercial outcomes
At RSM, we offer a competitive benefits and compensation package for all our people. We offer flexibility in your schedule, empowering you to balance life's demands, while also maintaining your ability to serve clients. Learn more about our total rewards at https://rsmus.com/careers/working-at-rsm/benefits.
Compensation Range: $126,500 - $254,700
Individuals selected for this role will be eligible for a discretionary bonus based on firm and individual performance.
