Lead Software Engineer (AI-Driven Development)

This role is four days onsite at our Seneca One Buffalo, NY location, with the flexibility to work from home one day per week

Responsible at the expert level for writing code and for the overall team's technical requirements gathering. Completes work following banking technology standards and contributes to the overall stability and resiliency of banking technology within the Software Development Lifecycle (SDLC), while also coaching others. Leads the team's adoption of AI-driven development — using approved AI coding assistants and agentic tooling to raise delivery speed and code quality — and is accountable for the correctness, security, and auditability of all AI-assisted output.

Technical Focus Areas

Expert-level coding in a minimum of 1 programming language and professional proficiency in a minimum of 1 additional language

AI-assisted development with approved tools (e.g. Claude Code, GitHub Copilot, IDE-integrated assistants) across the full SDLC

Agentic workflows : task decomposition, tool use, and multi-step agent runs applied to build, test, refactor, and migration work

Prompt and context engineering : repository-level instruction files, reusable prompts, skills, and Model Context Protocol (MCP) integrations

AI output governance : human-in-the-loop review, provenance and attribution, data handling, and secure-use guardrails for regulated environments

Test automation and evaluation, including AI-generated test coverage and regression safety nets

Developer experience and engineering productivity measurement

Primary Responsibilities

Develop and coach other team members while also working independently on the applications within the boundaries of approved Software Development Life Cycle (SDLC) processes, including identification and recommendation of metrics to solve business needs and minimize risks to the Bank through clean and resilient code.

Lead the team's adoption of approved AI-driven development tooling. Set the working patterns for where AI assistance is appropriate, where it is not, and what evidence is required before AI-assisted code enters the production deployment path.

Author and maintain the team's shared AI development assets — repository instruction files, reusable prompts, skills, agent definitions, and MCP server configurations — and treat them as reviewed, version-controlled code.

Review and approve the designs and proposals of pull requests, including AI-assisted and AI-generated changes, and oversee the execution on the change management of the request. Ensure every AI-assisted change is human-reviewed, understood by its author, and traceable to a requirement.

Design the process for peer review and coach others on code reviews, code quality, and improvements. Extend the review process to cover AI-specific failure modes, including hallucinated APIs, silent behavior changes, unvetted dependencies, licensing risk, and over-generated code that increases maintenance cost.

Author organized, clean, efficient, and secure source code at the expert level in a minimum of one programming language and moderate proficiency in a minimum of one other language, while coaching team members on the appropriate data structures and algorithms to solve business problems.

Oversee the source control management inclusions of other team members and perform the merging of code developments into the production deployment path.

Review testing methodology and code coverage of team members to ensure comprehensive testing is accomplished, and coach others on areas of opportunity. Use AI tooling to expand test coverage and generate regression cases, while validating that generated tests assert real behavior rather than restating the implementation.

Drive implementation adherence with patterns and practices for performance testing, resiliency, and optimization within their domain.

Apply AI-driven development to reduce toil in the domain — large-scale refactors, framework and dependency upgrades, legacy code comprehension, documentation generation, and incident and log analysis — and measure the outcome rather than the activity.

Review and approve requirement plans on behalf of the team in alignment with delivery partners, and ensure requirement completeness, accuracy, and feasibility.

Ensure all use of AI tooling complies with the Bank's data classification, privacy, third-party, and model-usage policies. Confirm that confidential customer, employee, and proprietary data is not exposed to unapproved services, and escalate exceptions.

Serve as a key partner with other groups; suggest continuous process improvement spanning organizational boundaries where, in their own judgement, technical or architectural concerns require it.

Contribute technical requirements where appropriate in support of delivery and maintenance solutions within their domain, including requirements for AI-enabled application features where the domain calls for them.

Understand and effectively communicate architecture, progress, and challenges to business or technical stakeholders and teams, including a clear account of where AI assistance was used and what controls applied.

Contribute ideas and recommendations to the team's application architectural design work, engineering practices, and pattern changes, and present at architecture peer group and architecture review board meetings within their domain.

Present the team's work at internal meetings with other peers and author internal blog posts. Share AI-driven development practices, measured results, and failure cases so other teams can reuse what works.

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.

Scope of Responsibilities

Supervisory / Managerial Responsibilities

No supervisory responsibilities.

Education and Experience Required

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 years' education and/or relevant work experience, including a minimum of 5 years' systems analysis and/or application development work experience.

Expert proficiency in a minimum of 1 relevant programming language and professional proficiency in a minimum of 1 additional relevant programming language.

Hands-on production experience using AI coding assistants as part of a normal delivery workflow, with the judgement to know when to accept, correct, or reject generated output.

Education and Experience Preferred

Expert analytical skills specific to application development

Experience leading a team's adoption of AI-assisted development tooling, including standards, guardrails, and enablement

Experience with agentic tooling and Model Context Protocol (MCP) integrations, prompt and context engineering, or evaluation of model output quality

Familiarity with AI governance in a regulated industry: data classification, model risk, third-party review, and audit evidence

Experience working in a team environment

Ability to work autonomously

Ability to multitask on multiple complex projects

Strong organizational skills

Strong time management skills

Advanced verbal and written communication skills

Experience driving project milestones and delivery dates

Proven experience leading a project or initiative

Experience partnering with and presenting to multiple stakeholders within technology and business departments

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