AI Automation Process Assurance Engineer I

About the Role

The AI Automation Process Assurance Engineer I is an individual contributor within the Software Process Assurance (SPA) team, combining AI and automation solution development with core process assurance responsibilities.

Key Responsibilities

AI Automation Engineering for Software Process Assurance

• AI-Assisted Code Review: Enhance, maintain, and scale AI-enabled code and artifact review workflows that evaluate software changes against coding standards, internal process procedures, and software requirements.
• Automated Requirements Traceability: Develop, maintain, and enhance automated scripts and workflows that verify bidirectional requirements traceability across engineering systems, flagging coverage gaps and broken links early in the SDLC.
• Defect Pattern & Recurrence Analytics: Apply AI and data analytics techniques to historical defect logs, audit findings, and incident records to detect recurring failure patterns and support proactive issue prevention.
• KPI & Trend Reporting Automation: Build and maintain automated data pipelines, dashboards, and anomaly-detection alerts for SPA metrics generation, trend identification, and data-driven decision-making.
• Cross-Functional Automation Support: Identify opportunities for AI augmentation within SPA and, as capacity and priorities allow, support broader AI-enabled automation initiatives across partnering departments.

Core Software Process Assurance & Operational Execution

• Process Improvement Implementation: Partner with engineering teams and SPA leadership to implement, tailor, and standardize documented software processes, procedures, work instructions, and quality checklists.
• Root Cause & Corrective Action (RCCA): Lead or participate in RCCA investigations for non-conformances and repeat defects, facilitating root cause analysis, corrective/preventive action (CAPA) tracking, effectiveness verification, and lessons-learned sharing.
• KPI Monitoring & Get-to-Green Plans: Track, analyze, and report SPA key performance indicators in partnership with metric owners; support the execution and tracking of get-to-green recovery plans.
• Lifecycle Compliance & Audit Support: Conduct targeted process audits, phase-gate reviews, and compliance assessments—while ensuring inspection-ready objective evidence—to verify adherence to internal QMS procedures and applicable customer, OEM, and regulatory standards (e.g., AS9100, DO-178).
• Cross-Functional & Global Collaboration: Collaborate with offshore, cross-functional, and Global Quality counterparts to resolve process gaps and ensure consistent SPA execution across programs.

Requirements & Tech Stack

• Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Systems, Quality Engineering, or a related technical field required.
• Entry-level or early-career experience (typically 0–2 years) in software development, scripting/automation, data analytics, process assurance, engineering governance, or process improvement. Relevant internships, academic capstone projects, or applied AI/automation projects will be considered.
• Exposure to Agile/SAFe software development methodologies, requirements management, version control/CI/CD workflows, or regulated engineering standards such as AS9100 or DO-178 is preferred.
• Programming & Automation: Hands-on experience or coursework in programming/scripting (e.g., Python, SQL, or similar) and developing or maintaining automation scripts, API integrations, or data workflows.
• Applied AI & Data Analysis: Working familiarity with LLMs, prompt engineering, AI-assisted analysis, or machine learning techniques for pattern recognition, trend detection, and workflow automation.
• Process Assurance & Engineering Governance: Foundational understanding of process assurance, SDLC governance, compliance verification, requirements traceability, configuration management, process improvement, and RCCA methodologies.
• Data Visualization & Enterprise Tools: Experience with data reporting and visualization tools (e.g., Power BI, Grafana, Excel) and familiarity with ALM/work management platforms (e.g., Jira, Jama, GitLab).
• Communication & Cross-Functional Collaboration: Strong analytical, organizational, and communication skills with the ability to collaborate across engineering, process assurance, project management, and other partnering functions.
• Responsible Automation Mindset: Proactive, builder-oriented approach to applying AI and automation while maintaining appropriate human review, engineering judgment, and accountability.

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Systems, Quality Engineering, or a related technical field required.
• Entry-level or early-career experience (typically 0–2 years) in software development, scripting/automation, data analytics, process assurance, engineering governance, or process improvement. Relevant internships, academic capstone projects, or applied AI/automation projects will be considered.
• Exposure to Agile/SAFe software development methodologies, requirements management, version control/CI/CD workflows, or regulated engineering standards such as AS9100 or DO-178 is preferred.
• Programming & Automation: Hands-on experience or coursework in programming/scripting (e.g., Python, SQL, or similar) and developing or maintaining automation scripts, API integrations, or data workflows.
• Applied AI & Data Analysis: Working familiarity with LLMs, prompt engineering, AI-assisted analysis, or machine learning techniques for pattern recognition, trend detection, and workflow automation.
• Process Assurance & Engineering Governance: Foundational understanding of process assurance, SDLC governance, compliance verification, requirements traceability, configuration management, process improvement, and RCCA methodologies.
• Data Visualization & Enterprise Tools: Experience with data reporting and visualization tools (e.g., Power BI, Grafana, Excel) and familiarity with ALM/work management platforms (e.g., Jira, Jama, GitLab).
• Communication & Cross-Functional Collaboration: Strong analytical, organizational, and communication skills with the ability to collaborate across engineering, process assurance, project management, and other partnering functions.
• Responsible Automation Mindset: Proactive, builder-oriented approach to applying AI and automation while maintaining appropriate human review, engineering judgment, and accountability.