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Dust is hiring a Global Head of AI Deployment & Partner Delivery to scale how enterprise customers successfully deploy AI agents into their operations. This is a player-coach leadership role combining strategic vision with hands-on execution.
You will lead Dust's global AI Deployment team, directly manage complex customer transformations, and build a partner delivery network that enables Dust to scale deployments without bottlenecking on internal resources. Your responsibilities span three core areas:
**Team Leadership & Growth**: Lead and develop Dust's existing global deployment team across regions and customer segments. Hire deployment leaders and practitioners as demand grows, shape career paths and coverage models, coach teams through complex situations, and establish operating rhythms and capacity planning to support scaling.
**Deployment Methodology & Standards**: Build a unified deployment method—from discovery through handoff—that both Dust and partner teams follow consistently. Define which deployments Dust leads versus partners deliver, establish entry/exit criteria and go-live readiness checks, and track delivery health metrics (time to first value, go-live quality, customer satisfaction). Run regular delivery reviews to spot at-risk engagements early, and convert lessons learned into reusable playbooks, templates, and reference architectures.
**Strategic Customer Deployments**: Personally lead complex, high-priority, and first-of-their-kind deployments. Help customer leaders define AI transformation for their organization by mapping current workflows and identifying where agents create the most value. Align executives, IT, security, and business teams on goals and success measures. Design change programs including executive sponsorship, communication plans, role-based enablement, and adoption milestones. Develop AI operators and champions inside customer teams, and track adoption post-launch before handing off to Customer Success.
**Partner Network Development**: Work with Partnerships to assess partner delivery capability, technical depth, and customer readiness. Start with a small, carefully selected group and grow capacity as quality is proven. Co-create onboarding, training, certification, and train-the-trainer programs. Co-deliver early partner engagements, then help partners progress toward independent delivery. Ensure customers experience Dust and partners as one aligned team.
Dust is a Sequoia-backed AI agent platform used by companies like Datadog, 1Password, Cursor, Clay, Vanta, and Persona. The company has 70%+ weekly active users and is targeting 5x growth by end of 2026. Dust operates with an office-first culture across Paris, London, San Francisco, and New York, with flexibility for remote work when it makes sense for the task at hand.
**Requirements**
- Minimum 6 years leading and developing customer-facing deployment or delivery teams, ideally across multiple regions or customer segments and through periods of fast change
- Proven track record leading complex enterprise deployments or transformation programs with measurable customer outcomes
- Experience building or significantly improving a deployment methodology, professional services model, or delivery function, adding structure to enable scaling without unnecessary process
- Demonstrated success leading organizational change alongside technology rollouts; built champion networks or adoption plans that changed how teams work after project completion
- Understanding of how systems integrators, consultancies, and implementation partners staff, deliver, and grow practices; experience building enablement or certification programs that created lasting capability in teams you didn't directly manage
- Track record turning around at-risk engagements (scope, staffing, technical complexity, stakeholder misalignment); judgment about when to standardize, adapt, or reset
- Credibility with customer executives, partner leaders, and technical practitioners; clear communication across all audiences
- Hands-on, low-ego leader with builder's instinct; preference for solving problems over handing off recommendations
- Comfort traveling to customers and partners when needed
- AI and technical credibility: ability to advise customers on where AI agents create meaningful value; understanding of how agents work in practice (prompting, context, tool use, retrieval, evaluations, reliability, cost); ability to challenge deployment assumptions about integrations, data connectors, authentication, permissions, security, and production readiness; capability to design evaluation approaches connecting AI performance to customer and business outcomes
- Note: You do not need to be a machine-learning engineer. Evidence of building high-performing delivery systems is prioritized over a perfect sequence of titles.
