VP, AI & Software Engineering

RR Donnelley•Chicago, IL

2d•$172,400 - $275,800•Remote

About The Position

The Vice President of AI & Software Engineering will shape the next era of our technology organization, leading the transformation from a traditional software engineering model to an AI-native engineering organization built on modern engineering practices and intelligent automation. This leader will drive AI across two critical dimensions: transforming how we engineer software through AI-assisted and agentic development practices, and building AI-powered solutions that create meaningful business value. The VP of Engineering will guide the evolution of our people, processes, applications, and technology platforms while honoring the strong foundation that has successfully enabled our business for more than 30 years. They will balance innovation with pragmatism by applying AI where it improves engineering effectiveness or business outcomes. As a strategic partner to the SVP of Digital Strategy, the VP of Engineering will help define our technology vision, engineering practices, AI strategy, governance, and investment priorities while ensuring disciplined execution, operational excellence, and sound financial stewardship.

Requirements

Bachelors Degree in Business, Computer Science, Information Systems, Industrial Management, Engineering, or related field, preferred
10-14 years of technical experience with 4-6 years of direct management of staff (managers and staff) OR demonstrated ability to meet the job requirements through a comparable number of years of applicable work experience.
Demonstrated experience leading enterprise application modernization, including the evolution of legacy and monolithic systems toward modular, API-first, cloud-ready architectures.
Deep understanding of the AI software engineering landscape, with experience applying AI-assisted and agentic approaches across the software development lifecycle.
Demonstrated ability to establish and evolve engineering standards, architecture principles, and governance.
Experience leading the evolution of engineering operating models and practices across established technology organizations.
Strong background in OpEx/CapEx management and the ability to tie technical milestones to financial outcomes.
Able to consistently contribute effort, leadership, and creative thinking to solving complex and significant problems in a collaborative fashion.
Must be able to demonstrate an ability to work concurrently on multiple complex and sometimes ambiguous problems.
Able to communicate complex concepts, problems, and solutions clearly and effectively to all levels within the organization.
Exceptional leadership abilities with a track record of building, managing, and motivating high-performing teams that are geographically distributed.
Strong business acumen and ability to understand and drive business objectives.
Strong organizational skills, including the ability to perform well under pressure and manage multiple priorities with competing demands for resources.
Requires excellent communication skills with all levels of audience. Able to structure messages in keeping with the listener’s experience, background, and expectations.

Nice To Haves

Navy MOS: IT:Information Systems Technician
Marine MOS: 0650:Network Operations and Systems Officer
Army MOS: 25B:Information Technology Specialist
Department: IT & Engineering
Coast Guard MOS: IT:Information Systems Technician

Responsibilities

Define and lead the adoption of AI across the software development lifecycle, establishing new engineering practices that combine human expertise with AI-assisted definition, design, development, and delivery.
Maintain a strong understanding of the rapidly evolving AI engineering landscape and continually adapt engineering practices, principles, and investment priorities as capabilities mature.
Establish measurable approaches for evaluating the impact of AI on developer productivity, software quality and reliability, and overall speed of delivery.
Ensure autonomous engineering agents operate within defined guardrails and modernization standards so generated code aligns with production and architectural expectations from the outset.
Architect applications and infrastructure to be “agent-ready”, systematically removing friction so automated workflows have increasingly higher-quality results over time.
Build the organizational capability to design, develop, and operate AI-powered business solutions, including agentic applications, AI assistants, workflow automation, and operations optimization tools.
Establish practical patterns for integrating AI capabilities into enterprise applications and workflows, including the platforms, APIs, enterprise data, security, and observability required to operate them effectively.
Establish rigorous practices for evaluating AI technologies and solutions based on business value, implementation reality, cost, and risk.
Create an engineering model that supports our idea-to-production pipeline, providing a disciplined path for moving successful concepts into production-grade solutions.
Provide technical leadership on emerging AI technologies and vendors, separating realistic business value from market hype and informing build, buy, partner, and platform decisions.
Serve as a key advisor to the SVP of Digital Strategy and other business and technology leaders, helping define and adapt the enterprise technology roadmap, engineering strategy, and architectural vision.
Advance modular, API-first, and composable engineering patterns that support rapid business delivery and seamless AI integration.
Maintain responsible stewardship of core legacy platforms, making deliberate investment decisions about where to modernize, re-architect, or retain.
Systematically optimize codebases, environments, and data access layers so autonomous tools and human teams can develop software safely and efficiently.
Shape the right mix of internal engineering talent, strategic partners, outsourcing, and augmentation for an AI-native organization.
Build and continuously develop the capabilities of the engineering organization, creating opportunities for existing talent to grow and succeed as software engineering practices and technologies evolve.
Identify emerging capability gaps and execute focused hiring strategies that bring critical engineering expertise into the organization.
Design and oversee effective support and sourcing models that maintain reliability while increasingly using automation and AI to improve efficiency.
Maintain excellence in financial discipline, managing the department budget with transparency and precision.
Evaluate technology investments based on measurable business and engineering outcomes rather than technology adoption alone.
Identify cost improvement opportunities through automation, engineering productivity, architectural efficiencies, application rationalization, optimized vendor management, and active management of cloud and AI unit economics.
Manage OpEx/CapEx effectively, connecting technology investments and engineering milestones to measurable financial and business outcomes.