Director, Engineering – AI Platform (Agentic AI)

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.
Partner with cross-functional stakeholders (Security, Risk, Legal, Infrastructure, Product) to drive safe and compliant AI adoption.
Advise executive leadership on emerging AI trends, risks, and enterprise opportunities.
Lead vendor evaluation, selection, negotiations, and ongoing management for AI platforms and tools.
Lead developer adoption, training, and governance frameworks to ensure responsible, effective use of AI across engineering teams.
Drive continuous improvement in engineering processes, quality standards, and platform capabilities.
Define and optimize model usage strategies across use cases, balancing performance, cost, and scalability (e.g., token consumption, model selection, and workload segmentation).
Evaluate and select AI tools, models, and platforms in a rapidly evolving landscape, aligning solutions to use case, cost, and performance requirements.

What you bring to the table

Proven experience implementing AI-enabled software development lifecycle transformation in production environments, including end-to-end integration and scaling across multiple engineering teams.
Strategic thinking with the ability to translate vision into execution
Strong leadership and team-building capabilities
Influencing and stakeholder management skills across all organizational levels
Advanced problem-solving and critical thinking abilities
Adaptability in a fast-changing, emerging technology landscape
Results orientation with a focus on measurable outcomes
Strong communication and storytelling skills for executive audiences
Collaborative mindset with a focus on cross-functional partnership

What’s needed- Basic Qualifications

Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or related field or equivalent work experience.
10+ years designing and delivering distributed systems in production environments.
5+ years leading managers and multi-level engineering teams.
2+ years building and scaling AI/ML or agentic systems, including multi-agent workflows and model lifecycle management.
Experience with at least one enterprise AI platform (e.g., Azure AI, AWS Bedrock, Google Gemini, Databricks).
Experience implementing agentic AI capabilities (e.g., orchestration, tool use, memory, evaluation).
Experience building scalable, reliable, and cost-efficient distributed systems.
Proficiency in one or more programming languages (Java, Python, TypeScript, or similar).
Demonstrated experience leading engineering teams, including managing managers and developing talent.
Strong cross-functional leadership and stakeholder influence skills.
Hands-on experience designing and deploying AI-enabled engineering platforms—not solely defining strategy.
Ability to translate complex technical concepts into executive-level insights.

What’s needed- Preferred Qualifications

Master’s degree in Computer Science, AI, Data Science, or related field
Experience with RAG, GraphRAG, vector databases, and enterprise knowledge integration patterns
Experience with AI orchestration frameworks (e.g., LangChain, LangGraph, LlamaIndex)
Experience implementing AI governance, responsible AI, and model risk management frameworks
Experience building secure AI platforms (e.g., access controls, audit logging, secrets management)
Experience defining and tracking engineering productivity or quality metrics tied to business outcomes
Experience le