Enterprise Architect, AI & Intelligent Platforms

M&T Bank•Buffalo, NY

1d•$139,700 - $232,900•Hybrid

About The Position

Provide enterprise architecture leadership for artificial intelligence, Generative AI, agentic systems, and intelligent platforms. Define the architectural foundation, governance, standards, and reusable patterns needed 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. It 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. The role requires on-site presence at the Seneca One Tech Hub in Buffalo, NY, for one week per month. It is a professional desk-based position with periodic business travel. The pay range is $139,700.00–$232,900.00 annually (USD); individual compensation will reflect 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 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, 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; AI platform, agentic, integration, and deployment reference architectures; RAG, enterprise context, and AI-ready data standards; Copilot extensibility patterns; model selection, evaluation, deployment, and lifecycle decision frameworks; AI risk and control overlays; agent registry and lifecycle standards; and AI observability, telemetry, cost, resilience, and incident-response standards.