Lead AI Platform Engineer
Role Summary
The Lead AI Platform Engineer is a hands-on technical contributor responsible for designing, developing, and integrating Generative AI-enabled capabilities within M&T Bank’s secure, governed enterprise environment. This role combines strong software engineering fundamentals with applied GenAI experience and partners closely with product, architecture, and engineering teams to deliver scalable, reliable, and responsible AI solutions.
Key Responsibilities
Design, develop, and support GenAI-enabled features including AI-assisted coding, documentation, testing, analysis, and workflow automation
Implement and maintain retrieval-augmented generation (RAG) solutions, including data ingestion, embeddings, vector stores, and search pipelines
Design and build agent-based and tool-invoking GenAI workflows, including multi-step LLM interactions using approved enterprise platforms
Develop and integrate APIs and backend services connecting LLMs to internal systems and approved third-party tools
Lead prompt design, testing, and iteration to improve output quality, relevance, and safety
Participate in model testing, evaluation, and performance analysis using established enterprise frameworks
Contribute hands-on development using modern languages and frameworks (e.g., Java, Python, C#)
Collaborate with Product Managers, Architects, and Engineers to translate requirements into technical solutions
Provide technical guidance and mentoring to less experienced engineers
Follow and promote M&T Bank SDLC, security, and responsible AI standards
Required Qualifications
Associate’s degree and a minimum of 7 years’ systems analysis and/or application development work experience or Bachelor's degree and a minimum of 5 years' systems analysis and/or application development work experience. In lieu of a degree, a combined minimum of 9 year’s education and/or relevant work experience, including a minimum of 5 years’ system analysis and/or application development work experience.
Strong foundation in software engineering principles, data structures, and algorithms
Hands-on experience with Java, Python, or C# in production environments
Experience with RESTful APIs, API-driven development, and Git-based version control
Working knowledge of machine learning and large language model (LLM) fundamentals
Experience working within enterprise SDLC, security, and compliance standards
Preferred Qualifications
Experience implementing Generative AI solutions in enterprise or regulated environments
Hands-on experience with RAG architectures, embeddings, and vector databases
Familiarity with Transformer-based models, attention mechanisms, and tokenization
Experience with Microsoft Azure, including Azure-based AI services and GenAI platforms
Exposure to CI/CD pipelines, DevOps tooling, and automated testing
Understanding of responsible AI, data privacy, and model governance principles