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 health scoring.

Partner enablement and scale through the ecosystem

- Work alongside AWS partners during live customer engagements to transfer knowledge in real time, not through slide-based training, but through joint problem-solving on production workloads.

- Contribute to development of partner delivery toolkits: scoped engagement models, technical validation checklists, and escalation criteria that allow partners to handle the 80% of blockers independently and route the remaining 20% with precision.

- Build the technical content and hands-on labs that form the backbone of partner certification and enablement programs for Bedrock adoption engagements; in collaboration with the Generative AI innovation center and other relevant teams.

Proactive value engineering

- Shift the CS motion from reactive support to proactive identification of unrealized value, benchmarking customer usage against peers, surfacing underutilized Bedrock capabilities, and building the two-page commercial case that gets placed in front of the right executive.

- Define and track adoption health metrics that go beyond technical KPIs (API calls, latency) to business-value KPIs (development velocity, cost per inference, time from PoC to production, use-case expansion rate).

- Conduct structured executive business reviews that connect Bedrock adoption metrics to customer business outcomes, not platform dashboards, but outcome narratives tied to the customer's own success criteria.

BASIC QUALIFICATIONS

- AWS certification, such as, AWS Solutions Architect, or a similar cloud certification

- 10+ years in senior technical customer-facing roles: solutions architecture, technical consulting, customer success engineering, or applied AI/ML engineering, with direct accountability for enterprise customer outcomes in production environments.

- Deep hands-on experience with large language model deployment and operationalization: model selection, prompt engineering, retrieval-augmented generation (RAG), fine-tuning, guardrails, agent orchestration, and inference cost optimization. You have built or directly supported production GenAI systems, not just evaluated them.

- Demonstrated experience building reusable technical assets, reference architectures, integration playbooks, assessment tools, cost models, that were adopted by field teams or partners to deliver at scale without your direct involvement.

- Strong understanding of enterprise integration patterns: API gateway architectures, identity and access management, data pipeline orchestration, security and compliance controls, and cost-management tooling in regulated or complex environments.

- Experience working alongside consulting partners (GSIs, SIs, or boutique technical consultancies) on joint customer engagements, not managing from a distance, but co-delivering technical work.

- Ability to translate technical architecture decisions into business-value language for executive audiences (VP, C-suite) and to translate executive priorities into actionable technical requirements for engineering teams.

PREFERRED QUALIFICATIONS

- PMP/SCRUM/Agile certification or are you SAFe certified

- Technical depth

- Hands-on experience with Amazon Bedrock, including multi-model management, Bedrock Agents, Knowledge Bases, Guardrails, and custom model import, in customer-facing delivery contexts, not just sandbox environments.

- Working knowledge of the broader AWS AI/ML and data stack: SageMaker, Lambda, Step Functions, Glue, OpenSearch, IAM, CloudWatch, Cost Explorer, and how these services compose into production GenAI architectures.

- Experience designing and implementing enterprise LLM proxy or gateway patterns, including token-level cost attribution, rate-limit management, model routing, and audit logging.

- Adoption and value realization

- Track record quantifying the business value of AI/ML adoption using metrics beyond usage volume - cost per inference, development velocity impact, time-to-production, and use-case expansion rate.

- Experience conducting maturity assessments and building adoption roadmaps for enterprise AI platforms, with structured approaches to identifying and removing blockers at each stage.

- Ecosystem and scale

- Experience building partner enablement programs with a technical core - hands-on labs, delivery toolkits, technical certification content - not just go-to-market collateral.

- Published or presented on topics in GenAI operationalization, enterprise AI architecture, or LLM deployment patterns at industry events, internal conferences, or through written thought leadership.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, CA, Mountain View - 210,200.00 - 284,300.00 USD annually

USA, CA, San Francisco - 210,200.00 - 284,300.00 USD annually

USA, GA, Atlanta - 182,800.00 - 247,300.00 USD annually

USA, MA, Boston - 182,800.00 - 247,300.00 USD annually

USA, NY, New York - 201,000.00 - 272,000.00 USD annually

USA, TX, Austin - 182,800.00 - 247,300.00 USD annually

USA, VA, Arlington - 182,800.00 - 247,300.00 USD annually

USA, WA, Seattle - 182,800.00 - 247,300.00 USD annually