AI Deployment Strategist

Role overview

Responsibilities

• Run technical discovery sessions to assess client data, systems, and AI readiness.
• Translate business problems into structured, buildable use cases and define success criteria before development.
• Draft proposal and SOW technical narratives covering architecture, risks, compliance, and delivery approach.
• Validate scope feasibility with the Field CTO and Solution Architect within budget and timeline.
• Manage client relationships, facilitate cross-functional workshops, and maintain the pursuit tracker.
• Frame organizational change and present the technical business case to client executives.

Requirements

• 15–20 years in technology or solutions consulting with deep AI/ML or data project experience.
• Technical grounding sufficient for credible architecture discussions.
• Experience with structured discovery methods, including design thinking, use-case prioritization, or jobs-to-be-done.
• Working knowledge of AI compliance frameworks, data residency, the EU AI Act, or sector regulations.
• Familiarity with Azure AI Foundry, AWS Bedrock, and Google Vertex AI for solution scoping.
• Understanding of realistic proof-of-concept to production delivery paths.

Communication and facilitation

• Clear executive-level writing and presentation skills are required.
• The role requires structured workshop facilitation, active listening, and diplomatic scope management.
• Open-source AI awareness is considered beneficial.

Success metrics

• Success is measured through proposal or SOW cycle time and quality, pursuit-phase client satisfaction, and scope accuracy at handover to delivery.