Elevate your career at EY, a global leader dedicated to building a better working world. We seek a highly experienced Senior Manager – AI Architect to spearhead enterprise AI strategy, architecture, and governance for data products and corporate finance transformation. This pivotal role merges AI/ML architecture expertise with strong technical and people leadership, translating complex business needs into secure, scalable, and responsible AI solutions.

You will be instrumental in integrating advanced AI capabilities, including predictive machine learning, generative AI, and controlled agentic workflows, with enterprise applications and data. The objective is to drive significant transformation across key financial processes such as Procure-to-Pay (P2P), Order-to-Cash (O2C), Record-to-Report (R2R), and Financial Planning & Analysis (FP&A).

This position involves owning the quality of AI architecture and technology roadmaps across various engagements. You will lead a team of architects and technical specialists, collaborating closely with data science, engineering, product, security, risk, and business stakeholders. Your guidance will steer solutions from initial discovery through successful production adoption and demonstrable value realization.

Shape the future of AI architecture by defining a clear strategy and roadmap, ensuring alignment with enterprise technology standards, data readiness, risk appetite, and core business objectives. Lead comprehensive discovery and feasibility assessments, expertly determining the optimal application of rules, analytics, traditional ML, or generative AI, and validating choices through measurable proof-of-concept studies.

Design robust, end-to-end AI architectures encompassing data ingestion, feature/embedding pipelines, model training/adaptation, retrieval, inference, orchestration, APIs, and seamless enterprise integration. Critically evaluate model providers, open-weight models, and cloud AI services, making informed build-versus-buy decisions that balance quality, latency, scalability, cost, licensing, data residency, and vendor dependencies. Establish definitive reference architectures and design standards for Retrieval-Augmented Generation (RAG), model serving, and agentic workflows, incorporating crucial elements like permission-aware retrieval, tool authorization, and human approval for consequential actions.

Drive AI delivery excellence by providing architecture oversight for all AI initiatives, clearly defining acceptance criteria, technical dependencies, delivery risks, and stage gates from experimentation to production readiness. Partner with engineering and platform teams to cultivate sophisticated MLOps and LLMOps practices, ensuring versioned datasets, models, and prompts, reproducible evaluation, stringent deployment controls, and effective rollback mechanisms.

Develop task-specific evaluation frameworks to rigorously assess predictive performance, groundedness, retrieval quality, hallucination risk, robustness, fairness, latency, and cost. Validate these frameworks against representative test data and established business baselines. Design production monitoring systems to track data and model drift, output quality, reliability, usage, and inference spend. Establish fallback behaviors, incident response protocols, and controlled procedures for model or provider updates. Embed responsible AI principles, privacy, security, and compliance controls in collaboration with risk and legal teams, covering data provenance, audit trails, prompt-injection defenses, sensitive-data protection, and risk-based human oversight.

A Bachelor's or Master's degree in Computer Science, Engineering, Data Science, AI, or a closely related field is required, alongside equivalent practical experience. You should possess over 12 years of comprehensive experience spanning software engineering, data platforms, solution architecture, or AI/ML, with a significant portion dedicated to architecting and deploying production-ready AI/ML solutions.

Demonstrate at least 5 years of technical people-management experience, showcasing a proven ability to lead architects or engineering teams and successfully deliver complex, cross-functional AI or data initiatives. Deep expertise in designing AI solutions on major cloud platforms such as Azure, AWS, or GCP is essential, including proficiency with managed AI services, deployment options, security, resilience, and cost optimization strategies.

Possess strong foundational knowledge in machine learning, model evaluation, and applied statistics, coupled with practical expertise in generative AI, Large Language Models (LLMs), embeddings, vector search, RAG, and the trade-offs involved in model adaptation. Advanced Python and software architecture skills are crucial, including experience with ML frameworks like PyTorch, TensorFlow, or scikit-learn, as well as APIs, data pipelines, and enterprise application integration. Familiarity with MLOps and LLMOps principles, encompassing experiment tracking, model registries, evaluation automation, CI/CD, model serving, observability, and lifecycle management, is expected.

A solid understanding of AI security and responsible AI practices is vital, including awareness of data leakage, prompt injection, model limitations, explainability, fairness assessment, access controls, and human-in-the-loop design. You must have a proven ability to guide AI solutions from initial use-case discovery and feasibility assessment through architecture, evaluation, production deployment, and operational transition. Experience in conducting architecture reviews, model and vendor evaluations, AI risk assessments, and production-readiness reviews across multidisciplinary teams is highly valued.