Deployment Strategist (Mid/Senior Levels)

About the role

Customer impact from day one. Strategic clarity and Execution that sticks. Salesforce is hiring Deployment Strategists — Strategic Builders who are customer-facing advisors, who define AI strategy, shape agentic solutions, and drive measurable adoption for enterprise customers. You'll work as part of a delivery team alongside Forward Deployed Engineers, translating complex business challenges into agentic AI deployments that go live and generate real value.

Responsibilities

• Full Lifecycle Ownership: Lead end-to-end customer engagement across the complete value lifecycle — Identify → Commit → Deploy → Consume — owning outcomes at every stage, not just discovery or deal closure
• AI Strategy & Architecture: Define the customer's Agentic AI strategy and roadmap. Lead design workshops, agent design sessions, and jobs-to-be-done analysis. Architect solutions spanning Salesforce's agentic AI platform, Data Cloud, and enterprise systems, with responsible AI governance baked in from the start.
• Deployment Oversight & Program Management: Manage engagement delivery end-to-end timelines, stakeholder communication, risk escalation, and progress reporting. You are the operational backbone of your delivery team, ensuring nothing falls through the cracks.
• Adoption & Consumption: Own adoption, consumption, and expansion metrics within your engagement scope. Identify blockers early and develop strategies to drive measurable value realization
• Delivery Team Coordination: Partner closely with the Forward Deployed Engineers on your team to align strategy with technical execution. Keep the team focused, unblocked, and on track to hit delivery milestones.
• AI-Supported Development: You'll be developing real customer solutions supported by leading-edge AI tools: Including Cursor, Claude, and our coding products like Vibes, to accelerate development.
• Customer Stakeholder Management: Serve as the primary customer-facing contact, building trust-based relationships with senior stakeholders and present proposals, decisions and outcomes in terms of business value, not just technical detail.
• AI Readiness: Assess customer readiness for AI adoption and guide change management and process transformation to maximise the benefits of intelligent agents
• Product Feedback Loop: Synthesise field insights and translate them into actionable product feedback that influences Salesforce's Agentic AI platform roadmap.

Requirements

• 4+ years experience in strategic solutioning, technical advisory, or delivery leadership or programme management for enterprise cloud or SaaS technology
• You have a degree in Computer Science or a related field
• Prior customer-facing delivery experience (consulting, professional services, or technical advisory) - this is a requirement, not a nice-to-have
• 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 to measurable outcomes
• Experience leading AI/LLM solution design, including agentic AI strategy, conversation design, and responsible AI practices
• Strong project and programme management skills — ability to run multiple workstreams, manage stakeholder expectations, and keep delivery on track in ambiguous environments
• Ability to travel 25% of the time to customer sites

Preferred Experience

• Experience with Salesforce Data Cloud, Agentforce platform, and Salesforce CRM (Sales, Service, Marketing)
• Experience designing and deploying AI solutions in regulated industries (e.g., public sector, financial services, healthcare)
• Salesforce platform certifications (e.g., Data Cloud Consultant, Agentforce Specialist, Application Architect) or similar platform certifications such as Microsoft, Oracle, or similar platforms
• Background in change management, organisational design, or enterprise transformation
• Experience with AI/ML concepts beyond LLMs, including NLP, RAG architectures, and model evaluation frameworks
• A get-things-done mindset with strong bias for action — comfortable operating in unstructured fast moving environments