Senior Software Engineer - Generative AI

Role overview

Responsibilities

• check_circle Design, develop, and support GenAI‑enabled features including AI‑assisted coding, documentation, testing, analysis, and workflow automation
• check_circle Implement and maintain retrieval‑augmented generation (RAG) solutions, including data ingestion, embeddings, vector stores, and search pipelines
• check_circle Design and build agent‑based and tool‑invoking GenAI workflows, including multi‑step LLM interactions using approved enterprise platforms
• check_circle Develop and integrate APIs and backend services connecting LLMs to internal systems and approved third‑party tools
• check_circle Lead prompt design, testing, and iteration to improve output quality, relevance, and safety
• check_circle Participate in model testing, evaluation, and performance analysis using established enterprise frameworks
• check_circle Contribute hands‑on development using modern languages and frameworks (e.g., Java, Python, C#)
• check_circle Collaborate with Product Managers, Architects, and Engineers to translate requirements into technical solutions
• check_circle Provide technical guidance and mentoring to less experienced engineers
• check_circle Follow and promote M&T Bank SDLC, security, and responsible AI standards

Basic qualifications

• Associate’s degree and a minimum of 5 years’ systems analysis and/ or application development work experience or Bachelor's degree and a minimum of 3 years’ systems analysis and/ or application development work experience. In lieu of degree, a combined minimum of 7 years’ education and/or relevant work experience, including a minimum of 3 years’ systems 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