Location
London

Hours
Full Time

Salary
Competitive salary commensurate with experience

About the Role
Salesforce is seeking Deployment Strategists - Strategic Builders who serve as customer-facing advisors defining AI strategy, shaping agentic solutions, and driving measurable adoption for enterprise customers. You will work closely with Forward Deployed Engineers as part of a delivery team, translating complex business challenges into agentic AI deployments that go live and generate real value. Engagements are focused and time-bound, typically lasting 12 weeks or less, and may involve a single strategic account or multiple customers simultaneously. Your impact extends beyond deployment to ensuring customers realize lasting, scalable value from the solutions your team builds. This role specifically supports Global Public Sector customers including Government, Nonprofit, and Education organizations.

Key responsibilities include full lifecycle ownership of customer engagements across the value lifecycle (Identify → Commit → Deploy → Consume), defining AI strategy and architecture, leading design workshops and agent design sessions, and architecting solutions spanning Salesforce's agentic AI platform, Data Cloud, and enterprise systems with responsible AI governance. You will manage deployment oversight and program management, ensuring timelines, stakeholder communication, risk escalation, and progress reporting are handled effectively. Driving adoption, consumption, and expansion metrics within your engagement scope is critical, as is coordinating closely with Forward Deployed Engineers to align strategy with technical execution. You will leverage AI-supported development tools such as Cursor, Claude, and coding products like Vibes to accelerate solution delivery. As the primary customer-facing contact, you will build trust-based relationships with senior stakeholders and present proposals and outcomes in terms of business value. Additionally, you will assess AI readiness, guide change management and process transformation, and provide actionable product feedback to influence Salesforce's Agentic AI platform roadmap.

Experience
- 4+ years in strategic solutioning, technical advisory, delivery leadership, or program management for enterprise cloud or SaaS technology
- Proven experience managing and delivering customer-facing projects within Public Sector, Higher Education, or Nonprofit environments
- Prior customer-facing delivery experience in consulting, professional services, or technical advisory is required
- Demonstrated understanding of the Salesforce platform and ecosystem, including Agentic AI capabilities and Data Cloud
- Working knowledge of one or more programming languages: Python, JavaScript, Java, or Apex
- Proven ability to lead and own complex, ambiguous engagements end-to-end from discovery through measurable outcomes
- Experience leading AI/LLM solution design, including agentic AI strategy, conversation design, and responsible AI practices
- Strong project and program management skills with ability to manage multiple work-streams and stakeholder expectations
- Ability to travel up to 25% to customer sites

About you
- A get-things-done mindset with a strong bias for action
- Comfortable operating in unstructured, fast-moving environments
- Strong interpersonal skills to build trust-based relationships with senior stakeholders
- Ability to synthesize field insights into actionable product feedback
- Passionate about AI and enterprise transformation

Qualifications
- Degree in Computer Science or related field
- Preferred experience with Salesforce Data Cloud, Agentforce platform, and Salesforce CRM (Sales, Service, Marketing)
- Salesforce platform certifications (e.g., Data Cloud Consultant, Agentforce Specialist, Application Architect) or similar certifications from Microsoft, Oracle, or other platforms are a plus
- Background in change management, organizational design, or enterprise transformation is advantageous
- Familiarity with AI/ML concepts beyond LLMs, including NLP, RAG architectures, and model evaluation frameworks