About Dust

Summary

Dust is hiring a Head of AI Deployment & Partner Delivery to make high-impact Dust deployments repeatable at scale.

When customers deploy Dust well, AI agents become part of how their teams work every day. Your job is to make that happen consistently, whether a deployment is led by Dust or by a trusted partner. You will lead our most complex customer deployments, turn what works, technically and organizationally, into clear playbooks, and build the partner delivery capacity that lets more customers succeed with Dust in 2027 and beyond.

This is a player-coach leadership role. You will lead and grow Dust’s global AI Deployment team, stay directly involved in our most important deployments, and build the methods, standards, and partner network that allow delivery to scale without depending on you for every engagement.

You will work closely with Sales, Customer Success, Solutions Engineering, Partnerships, Product, and Engineering to connect what customers want to achieve with how deployments are planned, delivered, and measured.

This is a chance to define how an AI-native company delivers transformation: what an exceptional deployment looks like, how value is measured, and how partners become delivery teams our customers trust.

What you’ll do

Lead and grow the global AI Deployment team

• Lead Dust’s existing global AI Deployment team and set its direction, priorities, and standards.

• Hire and develop deployment leaders and practitioners as customer demand grows.

• Shape roles, career paths, and coverage across regions, customer segments, and deployment types.

• Coach the team through complex customer situations and hold a consistently high bar for the work.

• Set up the operating rhythms, capacity planning, and reporting the function needs to scale.

Define how Dust deployments are delivered

• Build one deployment method, from discovery through handoff, that Dust and partner teams can both follow to the same standard.

• Define which deployments Dust should lead, which a partner can deliver well, and how work moves between the two.

• Set clear entry and exit criteria and go-live readiness checks, and track delivery health: time to first value, go-live quality, and customer satisfaction.

• Run regular delivery reviews, spot at-risk engagements early, and bring the right Dust and partner people together quickly when a deployment is blocked.

• Turn lessons from each engagement into reusable playbooks, templates, and reference architectures, and recurring friction into clear input for Product and Engineering.

Lead AI transformation with strategic customers

• Personally lead complex, high-priority, and first-of-their-kind deployments, from scoping to lasting adoption, bringing in Solutions Engineering, Product, and Engineering when complexity or risk calls for it.

• Help customer leaders define what AI transformation means for their organization: map how teams work today and identify the workflows where agents can create the most value.

• Align executives, IT, security, and business teams on goals, use cases, and success measures, with a baseline before launch and measured impact after.

• Design change programs for each deployment: executive sponsorship, communication plans, role-based enablement, and clear adoption milestones.

• Develop AI operators and champions inside customer teams, and help customers update ways of working, governance, and ownership so agents become part of how teams operate.

• Track adoption after launch, act quickly when teams aren’t changing how they work, and hand off to Customer Success with a clear picture of what comes next.

Build a high-quality partner delivery network

• Work with Partnerships to assess partners’ delivery capability, technical depth, and customer readiness.

• Start with a small, carefully selected group of partner practitioners and grow capacity as quality is proven.

• Co-create onboarding, training, certification, and train-the-trainer programs so partners can develop their own Dust experts.

• Co-deliver early partner engagements, then help partners progress toward delivering independently, leading adoption, not just implementation.

• Ensure customers experience Dust and its partners as one aligned team, with clear responsibilities on every engagement.

Requirements

• You have at least 6 years of experience leading and developing customer-facing deployment or delivery teams, ideally across multiple regions or customer segments and through periods of fast change.

• You have led complex enterprise deployments or transformation programs and delivered measurable customer outcomes.

• You have built or significantly improved a deployment methodology, professional services model, or delivery function, adding the structure needed to scale without unnecessary process.

• You have led organizational change alongside technology rollouts, and built champion networks or adoption plans that changed how teams work after the project ended.

  You don’t need to be a machine-learning engineer. Beyond that, we care more about evidence that you have built high-performing delivery systems than about a perfect sequence of titles.

• You understand how systems integrators, consultancies, and implementation partners staff, deliver, and grow a practice, and you have built enablement or certification programs that created lasting capability in teams you didn’t manage.

• You have turned around engagements at risk from scope, staffing, technical complexity, or stakeholder misalignment, and you know when to standardize, when to adapt, and when to reset.

• You are credible with customer executives, partner leaders, and technical practitioners, and you communicate clearly with all of them.

• You are a hands-on, low-ego leader with a builder’s instinct: you would rather solve the problem than hand off a recommendation.

• You are comfortable traveling to customers and partners when it matters most.

AI and technical credibility

• Advise customers on where AI agents can create meaningful value and where they can’t yet, and explain why to technical teams, business leaders, and executives.

• Understand how agents work in practice: prompting, context, tool use, retrieval, evaluations, reliability, and cost.

• Challenge a deployment plan’s assumptions about integrations, data connectors, authentication, permissions, security, and production readiness, and discuss the trade-offs with engineers.

• Design evaluation approaches that connect AI performance to customer and business outcomes.

Location

Why Dust

Learn how we think and work.

• Our product constitution, a story about our mission