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About This Role
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
M&T Bank is committed to fair, competitive, and market-informed pay for our employees. The pay range for this position is $116,400.00 - $194,000.00 Annual (USD). The successful candidate’s particular combination of knowledge, skills, and experience will inform their specific compensation. Location
Buffalo, New York, United States of America
Salary Context
This $116K-$194K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $192K across 146 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 8,943 AI roles we're tracking, AI Software Engineer positions make up 4% of the market. At M&T Bank, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,448 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($155K) sits 29% below the category median. Disclosed range: $116K to $194K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
M&T Bank AI Hiring
M&T Bank has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Remote, US, Buffalo, NY, US. Compensation range: $194K - $247K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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