Manager technology risk consulting - and emerging technology risk

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Role Summary

The Manager, AI & Emerging Technology Risk is a client-facing consulting leader who combines AI engineering and solution architecture with deep understanding of Risk functions (e.g., operational risk, model risk management, compliance, fraud/financial crime, credit risk, and enterprise governance). The role leads engagements to design, develop, and deploy production-grade AI/GenAI solutions that are secure, auditable, and aligned to regulatory expectations—while advising executives and technology teams on risk-by-design operating models, controls, and governance. The Manager partners with client engineering, data, and risk stakeholders to translate business and control requirements into implementable architectures, drive delivery from prototype to production, and operationalize monitoring and governance across data, models, and platforms.

Key Responsibilities

AI Engineering & GenAI Solution Delivery (Risk Use Cases)

• Design and implement AI/GenAI solutions for Risk use cases (e.g., risk intelligence, control testing, issue management, fraud detection, regulatory response) across data ingestion, feature engineering, model development, evaluation, and deployment
• Engineer secure reference architectures for AI platforms (cloud, data/feature stores, model registry, vector databases, API gateways) including GenAI patterns (RAG, tool use, agents) with embedded guardrails for access, prompt/data leakage, isolation, and resilience
• Operationalize Responsible AI and Model Risk practices through measurable tests (bias/fairness, robustness, explainability), documentation (model cards, data sheets), human-in-the-loop design, and continuous monitoring/drift management

Risk Governance, Controls & Regulatory Alignment

• Translate Risk requirements into technical control objectives and implementation details across the AI lifecycle (data, model, platform, SDLC), including evidence collection and audit-ready traceability
• Map AI/GenAI risks and controls to enterprise risk management (ERM) and technology risk frameworks, coordinating with Model Risk, Compliance, Privacy, and Security teams to meet policy and regulatory expectations
• Design and implement AI governance operating models (intake, use-case classification, approval gates, RACI, and exception handling) that integrate with SDLC/MLOps release processes for both ML and LLM-based systems

MLOps/LLMOps, Platform Engineering & Production Deployment

• Partner with client engineering and data teams to design end-to-end system flows (APIs, eventing, data pipelines) and integrate AI services into Risk platforms and workflows
• Build and assess MLOps/LLMOps practices including CI/CD, infrastructure-as-code, automated testing/evaluation, model/Prompt/versioning, and release gates aligned to Risk and control requirements
• Identify gaps in production readiness for AI systems (observability, drift/quality monitoring, secrets management, throughput/latency, failover, and incident response) and implement pragmatic remediation patterns

Advisory & Enablement

• Lead client workshops, discovery sessions, and design reviews to align Risk stakeholders and engineering teams on target-state AI/GenAI architectures and delivery approach
• Develop risk-focused training materials and deliver enablement sessions for technical and non-technical audiences, including executive briefings
• Coach and develop junior team members, providing technical oversight and quality control across AI engineering, governance, and risk deliverables
• Produce high-quality client deliverables (risk assessments, architecture patterns, implementation roadmaps, and executive summaries) with clear recommendations, trade-offs, and implementation steps
• Support engagement management by helping scope workstreams, define milestones, manage stakeholder expectations, and ensure on-time delivery and quality
• Contribute to business development through proposal writing, solution positioning, and creation of reusable assets (playbooks, accelerators, reference architectures) for Risk-focused AI offerings

Experience, Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Information Systems, or related field (or equivalent practical experience)
• 5–7+ years of experience delivering production technology solutions, with meaningful depth in AI/ML or GenAI engineering and experience in risk, compliance, audit, or other regulated environments
• Job relevant certification (i.e. Azure, Aws, or AI certifications)

Preferred Qualifications

• Hands-on experience with modern ML/GenAI stacks (e.g., Python, model development frameworks, embedding/RAG workflows) and the ability to design for evaluation, safety, and controllability
• Experience engineering and/or assessing AI platforms on cloud infrastructure, including IAM, encryption/secrets management, network controls, data governance/lineage, and environment segregation
• Strong software engineering fundamentals: APIs and service design, data pipelines, testing, code review, CI/CD, and operating production services
• Ability to translate architecture decisions into Risk impacts (control effectiveness, regulatory exposure, third-party risk, operational resilience) and define concrete technical mitigations
• Consulting delivery skills: requirements elicitation, workplan development, facilitation, and managing dependencies across client teams
• Experience producing model/GenAI governance artifacts (validation support, testing results, lineage, change logs) and partnering with Model Risk and audit stakeholders through approvals
• Executive-ready communicator with experience translating technical concepts into clear business and Risk narratives, influencing stakeholders, and presenting recommendations to senior leadership

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

All applicants will receive consideration for employment as RSM does not tolerate discrimination and/or harassment based on race; color; creed; sincerely held religious beliefs, practices or observances; sex (including pregnancy or disabilities related to nursing); gender; sexual orientation; HIV Status; national origin; ancestry; familial or marital status; age; physical or mental disability; citizenship; political affiliation; medical condition (including family and medical leave); domestic violence victim status; past, current or prospective service in the US uniformed service; US Military/Veteran status; pre-disposing genetic characteristics or any other characteristic protected under applicable federal, state or local law.

Accommodation for applicants with disabilities is available upon request in connection with the recruitment process and/or employment/partnership. RSM is committed to providing equal opportunity and reasonable accommodation for people with disabilities. If you require a reasonable accommodation to complete an application, interview, or otherwise participate in the recruiting process, please call us at 800-274-3978 or send us an email at [email protected].

RSM does not intend to hire entry level candidates who will require sponsorship now OR in the future (i.e. F-1 visa holders). If you are a recent U.S. college / university graduate possessing 1-2 years of progressive and relevant work experience in a same or similar role to the one for which you are applying, excluding internships, you may be eligible for hire as an experienced associate.

RSM will consider for employment qualified applicants with arrest or conviction records. For those living in California or applying to a position in California, please click here for additional information.

At RSM, an employee’s pay at any point in their career is intended to reflect their experiences, performance, and skills for their current role. The salary range (or starting rate for interns and associates) for this role represents numerous factors considered in the hiring decisions including, but not limited to, education, skills, work experience, certifications, location, etc. As such, pay for the successful candidate(s) could fall anywhere within the stated range.

Individuals selected for this role will be eligible for a discretionary bonus based on firm and individual performance.