Global Head of AI Deployment & Partner Delivery (US)

Dust•New York, NY

7d•$300,000 - $350,000•Hybrid

About The Position

Dust is hiring a Head of AI Deployment & Partner Delivery to make high-impact customer deployments repeatable at scale. This player-coach leadership role leads and grows Dust’s global AI Deployment team, personally guides the most complex and important deployments, and builds the methods, standards, and partner network needed to support more customers. The role works closely with Sales, Customer Success, Solutions Engineering, Partnerships, Product, and Engineering to connect customer goals with deployment planning, delivery, and measurement. The position establishes a consistent deployment method for Dust and partner teams, including criteria for delivery ownership, readiness, handoffs, and measurement of delivery health. It turns engagement lessons into reusable playbooks, templates, and reference architectures, and surfaces recurring customer friction to Product and Engineering. The role also leads AI transformation with strategic customers: defining valuable workflows, aligning stakeholders on goals and success measures, designing change and enablement programs, developing customer champions, and tracking adoption and impact after launch. In parallel, it builds a high-quality partner delivery network through partner assessment, onboarding, training, certification, co-delivery, and progression toward independent partner-led delivery. Dust prioritizes an in-person, office-first culture at its offices in Paris, London, San Francisco, and New York. Employees may work from home when it makes more sense for their work that day. The role requires travel to customers and partners when it matters most.

Requirements

At least 6 years of experience leading and developing customer-facing deployment or delivery teams.
Experience leading teams across multiple regions or customer segments and through periods of fast change is ideal.
Experience leading complex enterprise deployments or transformation programs and delivering measurable customer outcomes.
Experience building or significantly improving a deployment methodology, professional services model, or delivery function, adding structure that supports scale without unnecessary process.
Experience leading organizational change alongside technology rollouts and building champion networks or adoption plans that change how teams work after a project ends.
Understanding of how systems integrators, consultancies, and implementation partners staff, deliver, and grow a practice.
Experience building enablement or certification programs that create lasting capability in teams outside your direct management.
Experience turning around engagements at risk because of scope, staffing, technical complexity, or stakeholder misalignment.
Ability to judge when to standardize, adapt, or reset a deployment approach.
Credibility with customer executives, partner leaders, and technical practitioners, and clear communication with each.
Hands-on, low-ego leadership and a builder’s instinct; willingness to solve problems rather than only hand off recommendations.
Comfort traveling to customers and partners when needed.
Ability to advise customers on where AI agents can create meaningful value and where they cannot yet, and explain why to technical teams, business leaders, and executives.
Practical understanding of agents, including prompting, context, tool use, retrieval, evaluations, reliability, and cost.
Ability to challenge deployment assumptions around integrations, data connectors, authentication, permissions, security, and production readiness, and discuss trade-offs with engineers.
Ability to design evaluations that connect AI performance to customer and business outcomes.

Responsibilities

Lead Dust’s 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 maintain a consistently high bar for delivery.
Establish operating rhythms, capacity planning, and reporting for the deployment function.
Create a deployment method from discovery through handoff that Dust and partner teams can follow to the same standard.
Define which deployments Dust should lead, which partners can deliver, and how work transitions between them.
Set entry and exit criteria and go-live readiness checks; track time to first value, go-live quality, and customer satisfaction.
Run delivery reviews, identify at-risk engagements early, and coordinate Dust and partner resources to unblock deployments.
Turn engagement lessons into reusable playbooks, templates, and reference architectures, and communicate recurring friction to Product and Engineering.
Personally lead complex, high-priority, and first-of-their-kind customer deployments from scoping through lasting adoption.
Help customer leaders define AI transformation goals by mapping workflows and identifying where agents can create value.
Align executives, IT, security, and business teams on goals, use cases, success measures, and impact measurement.
Design deployment change programs, including executive sponsorship, communications, role-based enablement, and adoption milestones.
Develop AI operators and champions within customer teams and help customers update working practices, governance, and ownership.
Track adoption after launch, address stalled behavior change, and hand off to Customer Success with clear next steps.
Assess partner delivery capability, technical depth, and customer readiness with the Partnerships team.
Build partner capacity through a carefully selected practitioner group, onboarding, training, certification, and train-the-trainer programs.
Co-deliver early partner engagements and help partners progress to independent delivery that includes leading adoption, not only implementation.
Ensure Dust and partner teams present a coordinated customer experience with clear responsibilities.
Advise customers on where AI agents can and cannot create meaningful value and explain the reasoning to technical teams, business leaders, and executives.
Challenge deployment assumptions concerning integrations, data connectors, authentication, permissions, security, and production readiness; discuss trade-offs with engineers.
Design evaluation approaches that connect AI performance to customer and business outcomes.