AI Transformation Specialist — Amazon Bedrock, AWS Specialist and Partner Organization

AWS is hiring an AI Transformation Specialist to lead enterprise adoption of Amazon Bedrock across AWS's customers. This is a senior individual contributor role within the AWS Specialists & Partners (ASP) Customer Success Center of Excellence, requiring a combination of executive-level strategic vision, technical depth, and practical credibility to help customers move Bedrock from pilot to production and unlock tangible business value. In platform services like Bedrock, value is measured by production workload depth, use-case breadth, and the economic sustainability of token spend, a conceptually different challenge from the individual-level adoption that defines success in assistant and end-user products. You will define and evolve the metrics and methods that capture real business value in this context. You will work side by side with AWS partners in our most strategic customers inside their environments, diagnosing the adoption barriers that stall production rollouts, identifying priority use cases, building the business case that unlocks executive commitment, aligning engineering and business stakeholders around a shared path to value, and driving measurable outcomes that justify continued investment. You will convert what you learn into documented patterns, reusable assets, and repeatable mechanisms that partners and the AWS field can execute independently. Your goal is to solve the hard problems once, codify what works, and make it available at scale. Key job responsibilities Strategic customer transformation - Lead discovery and adoption assessments with enterprise customers to identify the patterns that accelerate Bedrock transition from pilot to production at scale, including cost-attribution models, governance frameworks, organizational readiness strategies, and use-case prioritization. Define the path forward with clear milestones and executive-aligned success criteria. - Own the value narrative for Bedrock adoption. Work with customer finance and engineering leaders to build a defensible ROI case for token spend — the argument customers lose is not whether Bedrock works, but whether the spend underneath can be allocated, forecasted, and justified. - Bring sufficient technical depth to earn credibility with customer engineering teams — understanding proxy architectures, guardrail implementations, agent orchestration patterns, and token-cost mechanics well enough to diagnose root causes, even when the hands-on build is done by the customer, partner, or SA. - Own cross-functional alignment between engineering, security, compliance, and business stakeholders to clear the enterprise-layer blockers that stall rollouts — ensuring decisions are made and adoption timelines hold. - Establish adoption health baselines and track progress against business-value KPIs (cost per inference, time from PoC to production, use-case expansion rate) — outcome measures tied to the customer’s own success criteria, not platform metrics alone. Asset creation and input to post-launch tooling - Convert every customer engagement into documented, reusable artifacts: discovery workshop models, cost-modeling templates, adoption maturity assessments, use-case prioritization tools, and blocker-resolution playbooks. - Build assessment tools that partners and field teams can use to diagnose a customer’s Bedrock adoption maturity, identify the specific blockers in play, and prescribe a remediation path without requiring specialist intervention. - Maintain a living catalog of resolved blocker patterns, indexed by industry vertical, enterprise architecture archetype, and use case — so the next customer with the same problem gets a validated path forward, not a fresh discovery engagement. - Provide structured input to the customer success intelligence team within the CS COE on tooling requirements, surfacing the data signals, adoption indicators, and blocker taxonomies that should be instrumented into post-launch monitoring and automated