Principal AI Delivery Lead

EBSCO Information Services (EBSCO) delivers a fully optimized research experience, seamlessly integrated with a powerful discovery platform to support the information needs and maximize the research experience of our end-users. Headquartered in Ipswich, MA, EBSCO employs more than 2,700 people worldwide, with most embracing hybrid or remote work models. As an AI-enabled service leader, we thrive on innovation, forward-thinking strategies, and the dedication of our exceptional team. At EBSCO, we’re driven to inspire, empower and support research. Our mission is to transform lives by providing reliable and relevant information — when, where and how people need it. We’re seeking dynamic, creative individuals whose diverse perspectives will help us achieve this global, inclusive mission. Join us to help make an impact.

Your Opportunity EBSCO is standing up a forward-deployed AI delivery capability inside its Data & AI organization - a small pod structure that turns real business problems into production AI systems and hands them back to the teams that own them. We are hiring a Delivery Lead to lead it.

This is a player-coach role for someone who can set technical direction for agentic AI systems, run a disciplined intake → build → hand-off delivery model, and build repeatable internal capabilities . You will lead a team of AI Engineers and partner with sponsors across Product, Security, Customer Service, and Operations to ship AI that measurably moves speed, quality, and cost. Just as important, you will leave behind reusable patterns, evaluations, and guardrails that let the rest of EBSCO move faster. You will join at the ground floor of a funded, executive-sponsored team with a live backlog waiting on day one !

What You’ll Do

• Lead the Forward-Deployed Engineering pod : O wn the outcomes, the delivery roadmap, and the growth and performance of the team
• Run the operating model : S core inbound demand on value / feasibility / data-risk, time-box engagements, and enforce a clean hand-off so the receiving team can own, operate , and extend what you build
• Set technical direction for production agentic systems : O rchestration and long-running workflows, RAG / enterprise search, human-in-the-loop, governed tool and data access (e.g., MCP), and bring-your-own-model patterns
• Stand up the delivery foundations : A n evaluation harness, responsible-AI guardrails, LLM observability, and a reusable pattern catalog that compounds across engagements
• Own cost-to-serve : M odel selection, small-language-model migration, prompt caching, and token economics
• Partner with named business sponsors to convert their use cases into shipped, adopted systems
• Work alongside strategic delivery vendors as accelerants while protecting EBSCO IP ownership
• Instrument and report impact on a Speed / Quality / Cost / Learning scorecard and communicate back to SLT
• Scale the capability : M ature the pod and seed the embedded champion (“spoke”) network so applied-AI delivery scales without the central team becoming the bottleneck

Your Team

You will lead the Forward-Deployed Engineering (FDE) team , the delivery engine of EBSCO’s AI operating model, within the Data & AI O rganization (DAIO) , reporting to the VP, Head of AI. You will work the enterprise’s highest-value applied-AI and automation use cases, backed by a standing backlog, direct executive sponsorship, and a clear mandate to define how AI delivery gets done at EBSCO. This is a build-it role, with the autonomy that comes with it.

About You

• 10+ years in software, product, or AI delivery, including 3+ years leading teams that shipped AI-powered production software
• Demonstrated ownership of production AI/ML or generative-AI systems end-to-end from solution design through production rollout and adoption not just prototypes or strategy decks
• Hands-on fluency with modern agentic AI such as LLM orchestration, RAG / vector search, human-in-the-loop design, prompt engineering, and agent evaluation
• Proven track record partnering with delivery and implementation teams to take enterprise use cases into production and hand them off cleanly
• Strong grounding in responsible AI, evaluation frameworks, and guardrails . Ability to define “done” for a production agent
• Cloud-native (AWS preferred ); comfortable setting architecture direction and earning the trust of engineers
• Player-coach mindset : Lead , manage, and represent the team to exe…