Enterprise Architect, AI & Intelligent Platforms

M&T Bank•Buffalo, NY

1d•$139,700 - $232,900•Hybrid

About The Position

M&T Bank seeks an enterprise architecture leader to define the strategy, architecture, governance, standards, and reusable patterns for artificial intelligence, Generative AI, agentic systems, and intelligent platforms across the enterprise. The role focuses on strategy, governance, and cross-enterprise transformation rather than day-to-day solution delivery. It aligns AI adoption with business strategy, enterprise capabilities, risk appetite, regulatory obligations, and the long-term operating model, establishing AI as a governed enterprise intelligence layer across platforms, processes, data, and decisioning. The Enterprise Architect will shape target-state platforms, integration patterns, data and context services, agent architectures, and operating-model implications. The role establishes guardrails for responsible adoption and translates business strategy into AI-enabled capability and investment roadmaps. It embeds security, privacy, model risk, resilience, explainability, auditability, regulatory compliance, and operational control into architectural patterns, while guiding platform and model choices based on value, cost, performance, interoperability, and risk. The position requires on-site presence at the Seneca One Tech Hub in Buffalo, NY, for one week per month. It is a professional desk-based role with periodic business travel. The role’s annual pay range is $139,700–$232,900 USD; specific compensation depends on the successful candidate’s knowledge, skills, and experience.

Requirements

Bachelor’s degree and 7 years of relevant work experience, or, in lieu of a degree, a combined minimum of 11 years of higher education and/or relevant work experience.
At least 3 years of hands-on experience with Azure OpenAI Service, Azure AI Studio, Microsoft AI Foundry, or a comparable enterprise AI platform, including production deployments.
Experience designing cloud-based AI architectures using Azure, AWS, or GCP with modern integration, event, identity, and API-management patterns.
Experience integrating AI/ML and LLM capabilities into enterprise systems with production-grade monitoring, scaling, resilience, security, and failover.
Working knowledge of Microsoft 365 Copilot extensibility, Microsoft Graph, connectors, plugins, and Copilot Studio.
Strong understanding of Responsible AI, evaluation methods, data governance, cybersecurity, privacy, model risk, and AI controls in regulated environments.
Demonstrated ability to influence senior business, technology, risk, and governance stakeholders and communicate complex tradeoffs clearly.

Nice To Haves

At least 5 years architecting AI/ML or Generative AI solutions in financial services, healthcare, or another highly regulated industry.
Hands-on experience with Microsoft AI Foundry, multi-agent orchestration, evaluation harnesses, guardrails, and reusable accelerator patterns.
Production experience with RAG pipelines, vector databases, embedding models, reranking strategies, grounding, and content-safety controls.
Experience integrating OpenAI, Anthropic Claude, or multi-model architectures with enterprise governance controls.
Practical experience with model risk governance, including Federal Reserve SR 11-7, OCC 2011-12, and related financial-services regulatory expectations.
Relevant certifications such as TOGAF, Microsoft Certified: Azure Solutions Architect Expert, or AI/ML specialty credentials.

Responsibilities

Define and maintain enterprise target-state architecture for AI, Generative AI, and agentic systems.
Establish enterprise principles, standards, reference architectures, and decision frameworks for AI adoption.
Translate business strategy into AI-enabled capability, platform, and investment roadmaps.
Define architecture for enterprise AI platforms and services, including Microsoft AI Foundry, Azure OpenAI, Microsoft 365 Copilot, Copilot Studio, Azure AI Search, and approved third-party model providers.
Establish patterns for model selection and orchestration, evaluation, prompt management, guardrails, telemetry, cost management, resilience, and lifecycle governance.
Determine when to use packaged copilots, platform-based workflows, custom agents, traditional AI/ML, or external models.
Define enterprise patterns for AI agents and multi-agent workflows, including tool use, workload identity, permissions, human-in-the-loop controls, monitoring, auditability, and kill switches.
Define retrieval-augmented generation and enterprise-context architectures, including standards for AI-ready data, metadata, lineage, sensitivity, access, provenance, vector search, and grounding.
Lead architecture for Copilot extensibility, Microsoft Graph connectors, plugins, and enterprise integrations, including identity, access control, data loss prevention, information protection, and data residency.
Embed Responsible AI, model risk, cybersecurity, privacy, content safety, prompt-injection defenses, third-party model risk, and regulatory controls into architecture patterns.
Define control overlays, risk tiering, evaluation requirements, approval workflows, evidence capture, AI incident response, and ongoing monitoring.
Lead AI architecture governance through design authorities, architecture reviews, and SDLC checkpoints, maintaining architectural accountability through production and lifecycle management.
Partner with business, data, technology, cybersecurity, risk, privacy, compliance, engineering, and platform teams to drive implementable decisions.
Explain investment, risk, and architecture tradeoffs to executive governance bodies.
Produce enterprise AI target-state architecture and capability-to-technology roadmaps; platform, agentic, integration, and deployment reference architectures; RAG, enterprise-context, and AI-ready data standards; and Microsoft 365 Copilot and Copilot Studio extensibility patterns.
Develop model selection, evaluation, deployment, and lifecycle decision frameworks; AI risk and control overlays, evidence requirements, and review templates; agent registry, ownership, permission, monitoring, and lifecycle standards; and AI observability, telemetry, cost, resilience, and incident-response standards.