Principal AI Platform Engineer

The Principal AI Platform Engineer serves as a senior technical authority responsible for designing, developing, and driving enterprise adoption of Generative AI capabilities within M&T Bank's secure, governed technology ecosystem. This role combines deep software engineering expertise, advanced AI platform knowledge, and strategic technical leadership to deliver scalable, resilient, and responsible AI solutions. The Principal AI Platform Engineer influences enterprise architecture, establishes engineering best practices, mentors technical talent, and partners with senior technology and business leaders to shape the future of AI-enabled capabilities across the organization.

• Design, develop, and support enterprise-scale GenAI solutions, including AI-assisted development, documentation, testing, analytics, and workflow automation capabilities.
• Lead the architecture, implementation, and optimization of Retrieval-Augmented Generation (RAG) solutions, including ingestion pipelines, embeddings, vector stores, retrieval frameworks, and search capabilities.
• Design, review, and approve agent-based and tool-integrated AI architectures, including multi-step LLM workflows using approved enterprise platforms and services.
• Develop and oversee APIs, shared services, and reusable frameworks that connect AI models to internal systems, platforms, and approved third-party tools.
• Establish prompt engineering standards, evaluation methodologies, and testing frameworks to continuously improve AI solution quality, reliability, safety, and performance.
• Drive model testing, benchmarking, evaluation, and performance analysis efforts utilizing enterprise-approved frameworks and governance standards.
• Author organized, secure, scalable, and maintainable code at the expert level using multiple programming languages, including Java, Python, and C#, while promoting engineering excellence across teams.
• Lead technical discussions with Product Managers, Architects, Engineers, and senior stakeholders to translate business objectives into enterprise-scale AI solutions.
• Serve as a subject matter expert for Generative AI, AI platform engineering, and responsible AI practices across Technology.
• Mentor and coach engineers on software engineering best practices, AI architecture patterns, algorithms, data structures, and enterprise platform design.
• Contribute to technical roadmaps that balance strategic AI initiatives, platform modernization, innovation, operational excellence, and technical debt reduction.
• Define and champion architecture standards, design patterns, and implementation guidance for AI-enabled solutions across multiple technology domains.
• Apply and analyze engineering, operational, and AI performance metrics to identify opportunities for process improvement, platform optimization, and increased adoption.
• Lead the implementation of resiliency, performance, observability, and security best practices within AI platforms and services.
• Drive adoption of responsible AI, model governance, security, compliance, privacy, and risk management standards throughout the AI development lifecycle.
• Participate in and present to enterprise architecture forums, engineering councils, and leadership committees while influencing AI strategy and technology direction.
• Understand and adhere to the Company's risk and regulatory standards, policies and controls in accordance with the Company's Risk Appetite. Identify risk-related issues needing escalation to management.
• Promote an environment that supports a culture of belonging and reflects the M&T Bank brand.
• Maintain M&T internal control standards, including timely implementation of internal and external audit points together with any issues raised by external regulators as applicable.
• Complete other related duties as assigned.

No supervisory responsibilities.

• Associate’s degree and a minimum of 9 years’ systems analysis and/ or application development work experience or Bachelor's degree and a minimum of 7 years' systems analysis and/ or application development work experience. In lieu of a degree, a combined minimum of 11 years’ education and/or relevant work experience, including a minimum of 7 years’ systems analysis and/ or application development work experience.
• Expert proficiency in a minimum of 1 relevant programming language and advanced proficiency in a minimum of 1 additional relevant programming language.
• Strong foundation in software engineering principles, data structures, algorithms, and distributed system design.
• Hands-on experience developing production applications using Java, Python, C#, or other modern enterprise programming languages.
• Experience designing and integrating RESTful APIs, microservices, and API-driven architectures.
• Advanced knowledge of Generative AI, Large Language Models (LLMs), prompt engineering, and AI application development.
• Experience building and supporting AI-enabled applications within enterprise SDLC, security, compliance, and governance frameworks.
• Proven ability to influence technical direction, engineering standards, and architectural decisions across multiple teams.

• Experience implementing enterprise Generative AI solutions within financial services or other highly regulated industries.
• Deep expertise in Retrieval-Augmented Generation (RAG), embeddings, vector databases, retrieval frameworks, and semantic search technologies.
• Strong understanding of Transformer architectures, attention mechanisms, tokenization strategies, and model evaluation techniques.
• Experience designing AI agents, tool-integrated workflows, and advanced LLM orchestration frameworks.
• Expertise with Microsoft Azure, Azure AI Services, OpenAI technologies, and cloud-native AI platforms.
• Experience establishing enterprise AI governance, responsible AI practices, and model risk management controls.
• Experience leading large-scale technical initiatives, platform modernization efforts, or enterprise capability rollouts.
• Proven experience influencing senior technology and business stakeholders and driving enterprise-wide adoption of new technologies.
• Ability to work autonomously while leading complex technical initiatives across multiple teams.
• Advanced verbal and written communication skills with the ability to present complex technical concepts to executive audiences.
• Proven subject matter expertise in AI platform engineering, software architecture, and enterprise application development.
• Experience with CI/CD pipelines, DevOps tooling, automated testing, observability, and platform reliability engineering practices.

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