Manager, Technology Risk Consulting - Artificial Intelligence and Emerging Technology Risk

We are the leading provider of professional services to the middle market globally, our purpose is to instill confidence in a world of change, empowering our clients and people to realize their full potential. Our exceptional people are the key to our unrivaled, culture and talent experience and our ability to be compelling to our clients. You'll find an environment that inspires and empowers you to thrive both personally and professionally.

There's no one like you and that's why there's nowhere like RSM.

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 work streams, 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,…