SDLC GenAI Automation & Tooling Integrations Engineer

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.

• Build and enhance GenAI tooling and agent-based capabilities that automate SDLC work while preserving governance, traceability, quality, and control.
• Design integrations across SDLC tool ecosystems, including Git Lab Duo, Git Lab, Jira, Zephyr, Service Now, Confluence, SharePoint, Sonar Qube, Power BI, and related engineering platforms.
• Automate SDLC artifact generation and validation, including requirements, acceptance criteria, test planning artifacts, traceability outputs, permit readiness artifacts, evidence packages, workflow summaries, release notes, and governance dashboards.
• Engineer AI context-setting, prompt templates, model routing, agent workflows, validation patterns, and quality gates to ensure generated artifacts meet SDLC standards.
• Support SDLC metric dashboards, telemetry, data pipelines, and reporting automation needed for governance, adoption, compliance, control effectiveness, and process improvement insights.
• Rapidly prototype, test, and iterate on AI-driven development workflows while maintaining architecture, security, observability, and operational-readiness discipline.

• 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 Git Lab Duo, Git Lab, Jira, Zephyr, Service Now, Confluence, SharePoint, Sonar Qube, 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, Git Lab repositories, merge requests, Service Now 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,…