AI, Data & Technology - AI-ML Director

Grant Thornton US is building a market-leading AI practice focused on practical, secure, and scalable outcomes for our clients, and we are hiring AI/ML Solution Architects to lead discovery phase through production delivery. Hiring in most major US cities. As an AI-ML Architect you will translate business objectives into end-to-end AI architectures across ML/AI, application integration, data and governance—defining target-state designs, reference patterns, and implementation roadmaps; guiding technology choices across cloud and modern stacks; and partnering with security, risk, and delivery leaders to ensure solutions are operable, compliant, and measurable. The ideal candidate brings the right blend of hands-on AI engineering credibility and consulting leadership—strong communication with executives and technical teams, experience designing AI solutions that integrate with enterprise systems, and a pragmatic approach to tradeoffs across accuracy, cost, latency, reliability, and risk—along with the ability to mentor teams and shape repeatable assets and accelerators. We are actively recruiting for: Manager level (5+ years) for architects, and Director level (7+ years) for senior architects who can set technical standards, drive quality and reliability, and help shape reusable patterns and accelerators. This role is specifically a strategic investment to grow our AI capabilities and advisory services. If you want your work to matter, this is the moment: we are not building “another AI consulting practice”—we are rewriting the playbook for how clients deliver on the promise of AI. We’re building a practice where teams love the pace, the craft, and the real-world impact. Day-to-day

: • Lead discovery workshops to clarify business objectives, constraints, and measurable success criteria for AI/ML initiatives • Translate

into end-to-end target-state architectures across data, ML/AI, application integration, security, and governance • Define pragmatic tradeoffs across accuracy, latency, cost, reliability, privacy, and risk—and communicate decisions to exec and engineering audiences • Design the data + model lifecycle (pipelines, training/finetuning, serving, monitoring, drift detection, retraining) and the required MLOps/LLMOps foundations • Establish integration patterns with enterprise systems (APIs/events/workflows, IAM, observability) so solutions are operable and supportable in production • Partner with security, privacy, and risk teams to embed controls (access, auditability, data handling, responsible AI) into solution designs • Produce core delivery artifacts (architecture diagrams, reference patterns, implementation roadmap, runbooks) and drive architecture reviews • Mentor teams and build reusable assets/accelerators (reference architectures, templates, evaluation scorecards) to scale repeatable delivery quality You have the following