Enterprise Architect, AI & Platforms

This role requires on-site presence at our Seneca One Tech Hub in Buffalo, NY, for one week per month. Founded in 1856, M&T Bank is a regional financial institution with a long-standing commitment to financial strength, community banking, and responsible innovation. The bank invests in technology, data, and processes that improve customer outcomes, strengthen resilience, and support sustainable growth.

Provide enterprise architecture leadership for artificial intelligence, Generative AI, agentic systems, and intelligent platforms. Define the architectural foundation, governance, standards, and reusable patterns required to scale AI safely, consistently, and strategically across the enterprise. This is an enterprise architecture leadership role focused on strategy, standards, governance, and cross-enterprise transformation rather than day-to-day solution delivery. The role aligns AI adoption with business strategy, enterprise capabilities, risk appetite, regulatory obligations, and the long-term operating model.

The role establishes AI as a governed enterprise intelligence layer embedded across platforms, processes, data, and decisioning, rather than as a collection of isolated tools.

Shape enterprise AI architecture by defining target-state platforms, integration patterns, data and context services, agent architectures, and operating-model implications. Establish guardrails through architecture principles, standards, reference patterns, risk tiers, and decision frameworks that enable responsible adoption. Enable business transformation by translating strategy into AI-enabled capability and investment roadmaps that augment, automate, or redesign processes. De-risk adoption by embedding requirements for security, privacy, model risk, resilience, explainability, auditability, regulatory compliance, and operational control.

Maintain platform and model optionality by guiding choices across enterprise AI platforms and model providers based on value, cost, performance, interoperability, and risk.

Define and maintain the 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.

Define when to use packaged copilots, platform-based workflows, custom agents, traditional AI/ML, or external models.

Define enterprise patterns for AI agents, multi-agent workflows, 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 while 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 and explain investment, risk, and architecture tradeoffs to executive governance bodies.

Enterprise AI target-state architecture and capability-to-technology roadmaps

AI platform, agentic, integration, and deployment reference architectures

RAG, enterprise context, and AI-ready data standards

Microsoft 365 Copilot and Copilot Studio extensibility patterns

Model…