Manager Model Risk Management Jobs in Texas (NOW HIRING)

Job Summary:
RSM US LLP is the leading provider of professional services to the middle market globally, empowering clients and people to realize their full potential. The Manager, AI & Emerging Technology Risk is a client-facing consulting leader responsible for designing, developing, and deploying AI solutions while ensuring compliance with regulatory expectations and managing risk across the AI lifecycle.
Responsibilities:
• 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
• 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
• 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
• 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
Qualifications:
Required:
• 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:
• 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
Company:
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