Director, Engineering

Our digital solutions team is more than a traditional IT organization. We are a team of passionate, collaborative, agile, inventive, customer-centric, results-oriented problem solvers. We are intellectually curious, love advancements in technology and seek to adapt technologies to drive Staples forward. We anticipate the needs of our customers and business partners and deliver reliable, customer-centric technology services.

The Director of Engineering, Agentic AI is responsible for defining and delivering the enterprise strategy for AI-enabled software engineering, with a focus on building a secure, scalable, and production-grade agentic AI platform across the software development lifecycle (SDLC). This role leads the transformation of engineering through AI-driven tools, workflows, and operating models that improve developer productivity, software quality, and speed to market.

This leader owns the end-to-end AI developer experience, including platform strategy, ecosystem integration, governance, and measurable outcomes. The role partners cross-functionally with Security, Risk, Legal, Infrastructure, and Product teams to enable responsible and scalable adoption of AI capabilities across the enterprise.

Staples is at an inflection point in applying AI to the software development lifecycle. While we have successfully deployed AI across customer-facing and enterprise functions, we are in the early stages of transforming how software is built, tested, and delivered. This role is critical to establishing a scalable, cost-efficient, and enterprise-grade AI engineering ecosystem.

What you’ll be doing: 

• Drive end-to-end transformation of the SDLC, ensuring AI is embedded across requirements, development, testing, deployment, and post-release observability—not just code generation.

• Design and implement secure, compliant AI platforms with embedded governance, guardrails, and auditability.

• Establish and scale AI-powered capabilities including code assistants, agentic workflows, test automation, and developer productivity tools.

• Build and integrate a developer productivity ecosystem spanning collaboration tools, workflows, knowledge systems, and AI platforms.

• Define and track outcome-based metrics for developer productivity, software quality, and operational effectiveness, leveraging telemetry, observability frameworks, and reporting to measure AI impact at scale.

• Ensure scalability, reliability, resiliency, and cost optimization of AI and distributed systems.

• Evolve engineering operating models, delivery practices, and standards to support AI-enabled development.