Lead Software Engineer; AI-Driven Development

Position: 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 languageAI-assisted development with approved tools (e.g. Claude Code, Git Hub Copilot, IDE-integrated assistants) across the full SDLCAgentic 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) integrationsAI 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…