• Own and enhance internal platforms • Maintain and enhance internally developed applications and tooling that supports terminology management, content creation, mapping, workflow automation, and content delivery. • Build and maintain integrations across internal applications, APIs, knowledge bases, databases, and AI services. • Contribute to the design and implementation of new automation and AI-enabled capabilities as business needs evolve. • Own operational support for AI-enabled applications and workflows in production, including troubleshooting, incident response, release coordination, and ongoing maintenance. • Manage application deployments, infrastructure configuration, monitoring, alerting, and operational support for AWS-hosted applications and services. • Investigate production issues, perform root-cause analysis, and implement durable solutions that improve reliability. • Support AI agents and workflow automation capabilities as they mature from pilot initiatives into scalable production solutions. • Develop and troubleshoot cloud-based workflows using AWS services such as Bedrock, Lambda, Glue, S3, IAM, CloudWatch, and MWAA/Airflow. • Implement testing, monitoring, and operational readiness practices that improve the quality and reliability of AI-enabled workflows. • Partner with clinical, terminology, product, data science, and engineering teams to improve AI-enabled workflows while maintaining appropriate human review and auditability. • Mentor team members and promote software engineering best practices for secure, maintainable, and production-ready systems.

Role Description The Process Engineer, Internal Audit is the architect of the modern audit function. This role is not about performing audits; it is about designing the systems that perform them. You will engineer, automate, and continuously optimize audit workflows by integrating Generative AI, agentic orchestration, and process engineering principles. Your goal is to move the department from "point-in-time" manual testing to a "continuous assurance" model that provides real-time risk insights. Responsibilities - Audit Architecture & Process Re-Engineering - Workflow Deconstruction: Analyze and map complex end-to-end audit processes (Risk Assessment through Issue Remediations) to identify "automation-first" opportunities. - Lean Audit Design: Apply Lean/Six Sigma principles to eliminate "waste" in the audit lifecycle, such as manual data entry, redundant evidence requests, and administrative overhead. - Scalable Solutions: Design standardized, modular workflow patterns that can be reused across Financial, IT, and Operational audit domains. - AI Agent Orchestration & Prompt Engineering - Intelligent Augmentation: Build and deploy AI-assisted workflows using Vellum AI, Copilot Studio, and Claude 3.5/4 to automate: - Control Mapping: Automatically mapping disparate business processes to COSO/NIST frameworks. - Automated Walkthroughs: Using AI to summarize meeting transcripts and draft narrative documentation. - Anomaly Narrative Generation: Converting complex data exceptions into plain-English audit findings for reports. - Library Management: Develop and maintain a "Prompt Registry" and "Agent Library" to ensure consistency in how AI interacts with sensitive audit data. - Automation & Ecosystem Integration - Agentic Workflows: Design "Agentic" automations in Microsoft Power Automate or Make.com that can reason through exceptions, route approvals, and update GRC platforms (e.g., AuditBoard, Workiva) without human intervention. - Tool Mastery: Advanced deployment of DataSnipper for automated document matching (e.g., matching 1,000s of Invoices to POs in seconds) and population integrity testing. - Real-time Risk Dashboards: Design and maintain Power BI or Tableau visualizations that provide a live "Risk Heatmap" based on automated testing results. - Governance, Privacy & "Auditability" - AI Governance: Ensure all AI solutions are "explainable" and meet corporate Model Risk Management (MRM) and data privacy (GDPR/CCPA) standards. - Validation Controls: Embed "Human-in-the-Loop" checkpoints into every automated process to ensure AI-generated output is validated by a subject matter expert. Qualifications - Education: Bachelor’s degree in Industrial Engineering, Information Systems, Data Science, or Accounting with a heavy technical focus. - Experience: 5–8+ years in Audit Transformation, Process Excellence, or Business Systems Engineering. - Technical Stack (2026 Proficiencies): - AI Platforms: Hands-on experience with LLM orchestration (Vellum, LangChain, or Copilot Studio). - Automation Hubs: Advanced proficiency in Power Automate, Zapier/Make, or n8n. Requirements - Industry Savvy: Strong background in manufacturing or organizations engaged in government contracting. - Modern Audit Toolkit: Hands-on experience with SQL, Python, DataSnipper or AI-based audit tools (e.g., Alteryx, MindBridge, or custom GPT agents). - Data: Proficiency in Data Governance, Datawarehouse (Snowflake/DataBricks). - Passion for AI, self-learning, willingness to experiment and implement solutions that are pushing the boundaries. Benefits - Comprehensive medical, dental, and vision plans. - Matching 401(k). - Unlimited PTO and paid holidays. - Parental/adoption leave. - Legal insurance. - Home technology stipend.

• Implement version control best practices, ensuring all application code, system configurations, and infrastructure code are stored in repositories. • Develop deployment automation for infrastructure, applications, and security changes using modern scripting tools. • Build and manage Continuous Integration (CI) pipelines to ensure rapid feedback on code changes. • Apply trunk-based development strategies to maintain short-lived branches and streamlined workflows. • Create and manage test automation suites for infrastructure and security features. • Support test data management, collaborating with security, data science, and software development teams to optimize automated testing environments. • Integrate security automation into DevOps pipelines, collaborating with InfoSec teams to implement preapproved security libraries and tools. • Communicate technical concepts clearly to internal and external teams. • Contribute to Agile team planning and execution of DevOps initiatives.

• Lead the design and implementation of enterprise DevOps solutions, aligning with business goals. • Define and enforce DevOps best practices across multiple teams, optimizing pipelines for scalability and efficiency. • Architect and manage automated infrastructure deployment in cloud environments. • Guide security integration into CI/CD pipelines, ensuring compliance with industry standards. • Optimize test automation strategies to enhance system reliability and deployment confidence. • Develop test data management solutions, ensuring high-quality data for automated testing. • Influence tooling and technology choices for version control, CI/CD, and infrastructure automation. • Provide mentorship and technical guidance to other DevOps Engineers. • Communicate DevOps strategy and progress to stakeholders and clients.