AI Deployment Strategist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI Deployment Strategist based in the United States. This role combines consulting, product thinking, workshop facilitation, and hands-on AI development to help large enterprises overcome delivery bottlenecks.

You will work directly with client teams to understand how work gets done, identify meaningful constraints, and turn those insights into actionable hypotheses.

You will prototype AI-powered solutions using coding agents and real data, then test them within live workflows.

The role is highly collaborative, pairing you with an Engagement Lead and Platform Engineer on individual client engagements.

You will work with mature, process-heavy organizations where adoption, measurable outcomes, and practical execution matter more than theoretical recommendations.

This is an early-career opportunity with significant ownership and a defined path toward leading engagements.

Remote work is available across the U.S., with regular travel to client sites required for in-person discovery and diagnosis.

Accountabilities

• Diagnose operational bottlenecks: Design and facilitate workshops, interviews, diagnosis sessions, and process-mapping exercises to understand how work moves through complex organizations and identify the constraints that have the greatest impact on delivery.
• Develop actionable hypotheses: Distinguish genuine workflow constraints from lower-impact frustrations, quantify problems where possible, and define hypotheses with clear mechanisms, metrics, and timeframes that can be tested.
• Prototype AI solutions: Build functional AI-powered tool prototypes using coding agents such as Claude Code, Cursor, or comparable platforms, working with real data, real tasks, and real workflows.
• Own AI workflows: Develop and refine prompts, agent flows, and evaluation approaches while determining whether AI-generated outputs are sufficiently useful, accurate, and reliable for testing.
• Drive adoption: Work directly with users, observe how they interact with new solutions, remove adoption barriers, and iterate until the technology is being used effectively in live work.
• Measure and adapt: Monitor results against defined hypotheses, identify meaningful signals, communicate when assumptions are not supported, and recommend practical pivots or next steps.
• Advise client stakeholders: Translate findings and prototypes into concise, decision-ready recommendations for sponsors and senior stakeholders, while helping prioritize the problems and opportunities worth pursuing.
• Build trusted relationships: Work closely with operators, leaders, and client sponsors to establish credibility, navigate complex organizations, and keep engagements moving despite organizational constraints.
• Collaborate within a delivery pod: Partner daily with an Engagement Lead and Platform Engineer while contributing to the overall success of each client engagement.
• Support continuous improvement: Bring a bias toward experimentation, speed, evidence, and practical implementation rather than extended analysis without action.

Requirements

• Professional experience: 2–5 years of experience in product management, UX design, service design, product design, digital product consulting, or a closely related discipline.
• Workshop facilitation: Demonstrated experience designing and facilitating workshops with real stakeholders and converting workshop outcomes into clear decisions or actions.
• Hands-on AI development: Experience building and shipping a working prototype using an AI coding agent such as Claude Code, Cursor, or a comparable tool; candidates should be able to describe or demonstrate what they built.
• AI fluency: Working knowledge of LLMs, AI agents, retrieval, evaluation, and the practical limitations of AI systems sufficient to design useful workflows around them.
• Product and problem-solving mindset: Ability to identify critical problems in ambiguous environments, structure complex situations, and move quickly from discovery to experimentation.
• Bias toward action: Comfortable shipping an imperfect but useful solution quickly, testing assumptions, and improving based on evidence and user feedback.
• Communication skills: Strong ability to communicate clearly with both working teams and senior stakeholders, including the confidence to ask challenging q…