– SDLC GenAI Automation & Tooling Integrations Engineer

M&T Bank Corporation; Buffalo (NY); On-site; USD 116,000 - 194,000; Full time

Benefits offered by this job

Competitive benefits

Volunteer time

Job summary

M&T Bank Corporation in Buffalo, NY seeks an SDLC GenAI Automation Engineer to design, build, and maintain GenAI-enabled tooling that automates SDLC activities, with strong governance and artifact validation. You will integrate diverse enterprise tools, build AI-driven workflows, and ensure quality and security across the SDLC.

You will partner with governance leads and tool owners to reduce manual effort, strengthen traceability, and deliver metrics dashboards that measure governance,

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.
• Strong foundation in software architecture, system design, API design, integration patterns, engineering best practices, secure coding, testing, CI/CD, and operational support.
• Hands‑on experience using GenAI models, coding assistants, prompt engineering, RAG/context engineering, model evaluation, or agent‑based automation to support software delivery outcomes.
• Experience integrating enterprise tools through APIs, webhooks, pipelines, service accounts, event‑driven patterns, or middleware.

Responsibilities

• Design, build, test, and maintain GenAI-enabled capabilities that automate SDLC activities such as requirements decomposition, design validation, testing support, evidence generation, SDLC adherence measurement, workflow summarization, and governance reporting.
• Develop agent-based workflows that use AI models to generate, validate, refine, and route SDLC artifacts while preserving required human review, approval, and audit evidence.
• Create reusable engineering patterns for context injection, prompt templates, grounding data, artifact validation, model evaluation, confidence scoring, and output quality controls.
• Leverage AI models as primary execution engines while maintaining architectural, quality, security, and operational oversight of generated outputs and automated actions.
• Partner with internal tool owners for GitLab Duo, GitLab, Jira, Zephyr, ServiceNow, Confluence, SharePoint, SonarQube, Power BI, and related platforms to design and build integrations that support SDLC automation.
• Build APIs, services, connectors, pipeline jobs, automation scripts, event-driven workflows, and data transformations needed to connect SDLC systems of record and supporting tooling.
• Support integration patterns that connect requirements, Jira work items, generated artifacts, test cases, Zephyr evidence, GitLab repositories, merge requests, ServiceNow permits/RFCs, and dashboard metrics.

Job description

Job Summary

Job Summary SDLC GenAI Automation & Tooling Integrations Engineer will play a key role in automating and modernizing the enterprise SDLC by designing, building, integrating, and enhancing GenAI-enabled tooling and engineering capabilities. This role will partner closely with the SDLC Program Governance Lead, SDLC BSAs, GenAI engineering stakeholders, internal SDLC tool owners, and Technology delivery teams to reduce manual SDLC effort, improve artifact quality, strengthen governance traceability, and embed controls where engineering work occurs. This engineer will help build and mature AI-driven workflows that support SDLC artifact creation, artifact validation, approval routing, metrics capture, governance reporting, and tool-based evidence generation. The role will require strong software engineering fundamentals, practical GenAI engineering experience, workflow orchestration skills, integration experience across enterprise tools, and the ability to maintain quality, security, and architectural oversight while leveraging AI models as primary execution engines. The role is expected to support the creation of SDLC metric dashboards and data pipelines that help measure SDLC governance, adoption, compliance, control effectiveness, process efficiency, and improvement opportunities. The engineer will also help ensure GenAI-generated outputs meet SDLC quality standards through context engineering, prompt/policy design, agent workflow design, validation routines, human-in-the-loop controls, and repeatable quality gates.

Key Responsibilities

GenAI-Enabled SDLC Automation Engineering Design, build, test, and maintain GenAI-enabled capabilities that automate SDLC activities such as requirements decomposition, design validation, testing support, evidence generation, SDLC adherence measurement, workflow summarization, and governance reporting. Develop agent-based workflows that use AI models to generate, validate, refine, and route SDLC artifacts while preserving required human review, approval, and audit evidence. Create reusable engineering patterns for context injection, prompt templates, grounding data, artifact validation, model evaluation, confidence scoring, and output quality controls. Leverage AI models as primary execution engines while maintaining architectural, quality, security, and operational oversight of generated outputs and automated actions. Design fail-safe and human-in-the-loop patterns for AI-assisted SDLC automation, especially where generated artifacts, workflow actions, approvals, or downstream publishing may affect compliance or delivery outcomes. Partner with internal tool owners for GitLab Duo, GitLab, Jira, Zephyr, ServiceNow, Confluence, SharePoint, SonarQube, Power BI, and related platforms to design and build integrations that support SDLC automation. Design and implement integrations for SDLC artifact creation, artifact publishing, test artifact generation, approval routing, evidence capture, dashboard reporting, workflow status synchronization, and traceability across tools. Build APIs, services, connectors, pipeline jobs, automation scripts, event-driven workflows, and data transformations needed to connect SDLC systems of record and supporting tooling. Support integration patterns that connect requirements, Jira work items, generated artifacts, test cases, Zephyr evidence, GitLab repositories, merge requests, ServiceNow permits/RFCs, and dashboard metrics. Engineer AI context-setting patterns so generated SDLC artifacts are grounded in approved standards, procedures, templates, examples, decision logic, and quality criteria. Build agent workflows that can identify incomplete context, generate clarification questions, detect artifact gaps, flag low-quality outputs, and route items for human review when needed. Lead the creation of SDLC metric dashboards by building data pipelines, data models, telemetry capture, reporting views, automated extracts, and integration points across SDLC tools.

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 architecture, system design, API design, integration patterns, engineering best practices, secure coding, testing, CI/CD, and operational support. Demonstrated experience designing, building, testing, and iterating software solutions rapidly using modern development practices and AI-assisted development workflows. Hands‑on experience using GenAI models, coding assistants, prompt engineering, RAG/context engineering, model evaluation, or agent-based automation to support software delivery outcomes. Demonstrated ability to orchestrate workflows across multiple AI models and tools, leveraging model‑specific strengths for optimal output quality, speed, and reliability. Experience designing and implementing multi‑step AI‑driven automation or agent‑based workflows with human review, validation, monitoring, and exception handling. Expertise in AI‑assisted debugging, including structured prompts, multi-model validation, root‑cause analysis, systematic edge‑case identification, vulnerability analysis, and output verification. Experience integrating enterprise tools through APIs, webhooks, pipelines, service accounts, event‑driven patterns, or middleware. Experience with SDLC, Agile delivery, DevOps, testing, change/release management, source control, artifact management, and production readiness practices. Strong communication, collaboration, problem‑solving, documentation, and stakeholder engagement skills.

Preferred Qualifications

Experience with GitLab Duo, GitLab, GitLab pipelines, Jira, Zephyr, ServiceNow, Confluence, SharePoint, SonarQube, Artifactory, Power BI, Azure AI Foundry, Copilot Studio, or similar tools. Experience building dashboards, data pipelines, telemetry, analytics, or governance reporting solutions for technology delivery, compliance, controls, DevOps, or SDLC programs. Experience developing agent‑based workflows, tool‑using agents, AI orchestration layers, prompt/template registries, model routing, evaluation harnesses, or AI control/observability patterns. Experience with Azure, Kubernetes, Terraform, Key Vault, managed identities, observability platforms, API gateways, CI/CD runners, secrets management, or enterprise cloud engineering. Certifications or demonstrated training in cloud engineering, software architecture, AI engineering, DevOps, ITIL, or related disciplines.

Compensation

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.

Company Culture & Benefits

Great companies have an enduring sense of purpose. At M&T, our purpose is a simple one: make a difference in people’s lives and uplift the communities we serve. M&T Bank Corporation is a financial holding company headquartered in Buffalo, New York. M&T’s affiliates offer advice, guidance, expertise and solutions across the entire financial spectrum, combining M&T Bank’s traditional banking services with the wealth management and institutional capabilities offered by Wilmington Trust. M&T Bank has a network of over 1,000 branches and 2,200 ATMs that span 12 states from Maine to Virginia and Washington, D.C. For more than 165 years, M&T has strived to take an active role in our communities and build long‑lasting relationships with our customers. We are a bank for communities—combining the capabilities of a large bank with the care of a locally focused institution. As an employer of choice, we are proud to offer competitive benefits. Our core values – integrity, ownership, collaboration, curiosity, and candor – drive the work we do. We seek to further build upon our record of success by bringing in top talent and fresh skill sets while continuing to support the growth and development of all our team members.

• competitive benefits ranging from medical and retirement to forty hours of paid volunteer time, each year.

EEO Statement

M&T Bank is unwavering when it comes to providing equal employment opportunities to all employees and applicants without regard to race, color, national origin, religion, ethnicity, sex, gender identity, age, disability, citizenship, pregnancy, veteran status, military status, marital status, sexual orientation, genetic information or any other characteristic protected under applicable federal, state or local laws.