Sr Manager AI Platforms

Saved job description

Work Model: Onsite, Framingham, MA

The Senior Manager AI Platforms will join the Enterprise Data, ML & AI organization and will be responsible for owning, evolving, and scaling Staples’ AI/ML platform capabilities, including GenAI platforms, agentic frameworks, and supporting infrastructure. This role is focused on enabling teams across Sales, Customer Service, Marketing, Merchandising, and other business units to build, deploy, and operate production‑grade AI solutions safely, reliably, and at scale.

This is a critical leadership role requiring strong technical depth, platform mindset, and cross‑functional collaboration. The Senior Platform Manager will define the platform vision and roadmap, connect current platform capabilities to future enterprise needs, and drive execution through an iterative, agile delivery model. This role partners closely with AI/ML Engineering, Data Engineering, Cloud Infrastructure, Security, Architecture, and Product teams, as well as external vendors and technology partners.

The purpose of this role is to accelerate AI adoption across the enterprise, reduce friction for teams building AI solutions, ensure strong governance and reliability, and maximize the business value of Staples’ AI investments.

What you’ll be doing:

Own and evolve the enterprise AI/ML platform strategy, including AI/ML platform capabilities, GenAI tooling, agent frameworks, and shared services

Define and maintain a clear platform roadmap that aligns business priorities with scalable, secure, and reliable technical capabilities

Drive the maturation of AI/ML platforms from early enablement to enterprise‑scale, production‑ready systems

Partner with engineering teams to: 

Translate business and product needs into platform capabilities and standards

Define architecture patterns, guardrails, and best practices for AI/ML development and deployment

Collaborate with product managers and business stakeholders to: 

Enable faster experimentation while ensuring production readiness

Balance short‑term delivery needs with long‑term platform sustainability

Prioritize platform initiatives based on enterprise impact, risk reduction, adoption, and ROI

Establish and track platform success metrics, including adoption, reliability, performance, cost efficiency, and developer productivity

Partner with Security, Privacy, and Compliance teams to ensure responsible AI, access controls, and governance are built into the platform

Manage vendor and partner relationships (e.g., cloud providers, AI platforms) to ensure optimal use of external technologies

Communicate platform vision, roadmap, and progress clearly to technical and non‑technical stakeholders

What’s needed – Basic Qualifications:

Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent work experience. 

8+ years of experience in platform management, product management, technology leadership, software engineering, data engineering, AI/ML engineering, or infrastructure leadership. 

4+ years of experience working with AI/ML platforms, MLOps, GenAI systems, model lifecycle management, or ML infrastructure in an enterprise environment. 

2+ years experience with Databricks and cloud AI platforms such as Azure, Google Cloud Platform, or AWS. 

Experience owning or delivering shared platforms, reusable services, or infrastructure capabilities used by multiple teams. 

Strong understanding of AI/ML development lifecycle, including experimentation, deployment, monitoring, model governance, and operational support. 

Experience working in agile delivery models and cross-functional technology environments. 

Strong communication skills, with the ability to translate complex technical concepts into clear business and executive-level narratives. 

Proven ability to partner with engineering, architecture, security, product, and business teams to deliver enterprise technology capabilities.

What’s needed – Preferred Qualifications:

 Experience with GenAI platforms, large language models, agentic AI frameworks, prompt orchestration, vector databases, retrieval-augmented generation, or AI application development patterns. 

Experience with Databricks, MLflow, Feature Store concepts, model serving, model monitoring, CI/CD, observability tools, and cloud-native infrastructure. 

Strong understanding of scalability, reliability, resiliency, observability, access controls, and cost management for enterprise platforms. 

Experience defining and enforcing platform standards, APIs, reusable patterns, governance models, and engineering best practices. 

Experience with responsible AI practices, model risk management, privacy controls, and enterprise AI governance. 

Ability to break down complex platform challenges into clear, iterative, deliverable capabilities. 

We Offer:

Inclusive culture with associate‑led Business Resource Groups

22 days of PTO and Holiday Schedule (7 observed paid holidays + 1 floating holiday)

Online and Retail Discounts, Company Match 401(k), Physical and Mental Health Wellness programs, and more!

The salary range represents the expected compensation for this role at the time of posting. The specific base pay may be influenced by a variety of factors to include the candidate's experience, skill set, education, geography, business considerations, and internal equity. In addition to base pay, this role may be eligible for bonuses, or other forms of variable compensation.

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