Customer Solutions Manager, Prototyping & Customer Engineering

📍 Job Overview

Job Title: Customer Solutions Manager, Prototyping & Customer Engineering

Company: Amazon

Location: Boston, Massachusetts, United States; Seattle, Washington, United States; New York, New York, United States; Dallas, Texas, United States

Job Type: Full-time

Category: Customer Engineering / Solutions Architecture / Program Management (AI/Cloud Focus)

Date Posted: April 29, 2026

Experience Level: 6+ Years (Mid to Senior Level)

Remote Status: Hybrid

🚀 Role Summary

• Lead strategic, end-to-end AI-focused customer engagements, from initial concept to final delivery, leveraging Amazon's Working Backwards methodology.

• Orchestrate complex customer interactions, facilitating Prototyping Engineers and Design Technologists in AI solution design, particularly within Generative AI and Agentic AI architectures.

• Drive adoption of AI-driven development tools to accelerate prototype delivery and validate technical feasibility of innovative solutions.

• Build and nurture relationships with key customer executives and AWS leadership through structured communication, operational reviews, and clear reporting on business impact and AI outcomes.

📝 Enhancement Note: This role bridges technical program management, customer solutions architecture, and AI innovation. The emphasis on "Prototyping & Customer Engineering (PACE)" and "Agentic AI" suggests a focus on rapid iteration, proof-of-concept development, and early-stage technology validation, rather than traditional, long-term project delivery. The "Customer Solutions Manager" title, combined with "Prototyping," implies a consultative approach to guiding customers through complex technical challenges.

📈 Primary Responsibilities

• Own the full lifecycle of strategic AI-focused customer engagements, from kick-off to delivery, ensuring alignment with customer outcomes and business objectives.

• Apply the Working Backwards methodology to define comprehensive solution requirements, establish clear success criteria, and manage engagement scope effectively.

• Facilitate Prototyping Engineers and Design Technologists in designing advanced AI solutions, including RAG architectures and agentic patterns, and coordinate the adoption of AI-driven development tools.

• Partner closely with Global Sales and Solutions Architecture teams to identify, qualify, and strategically position Generative AI and Agentic AI opportunities.

• Develop and maintain robust relationships with customer executives and AWS leadership through consistent communication, operational reviews, and impactful reporting on AI outcomes and business value.

• Establish and track key engagement metrics, monitor delivery velocity, and contribute to program-level Key Performance Indicators (KPIs) that measure overall success and impact.

• Develop and implement process improvements to enhance team efficiency, accelerate prototype delivery speed, and foster knowledge sharing across the PACE community.

• Proactively identify and mitigate risks specific to AI implementations, ensuring successful and timely outcomes for customer engagements.

• Apply product thinking principles to ensure AI prototypes are developed with a focus on responsible AI practices, production readiness, and long-term customer value.

📝 Enhancement Note: The responsibilities highlight a blend of strategic program management, technical facilitation, and client relationship management. The emphasis on "AI-driven development tools," "RAG architectures," and "agentic patterns" indicates a need for a deep understanding of cutting-edge AI technologies. The requirement to "apply product thinking" suggests an expectation to consider scalability, maintainability, and end-user experience.

🎓 Skills & Qualifications

Education:

Experience:

• Minimum of 6 years of experience leading large-scale, technical or engineering programs.

• Demonstrated track record of thought leadership, business case development, realizing customer benefits, and successful program completion.

• Minimum of 6 years of customer-facing experience, engaging with customer executives, technologists, or partners to solve business problems with advanced technologies.

Required Skills:

• Technical Program Management: Expertise in managing complex, technical programs, with a focus on innovation and emerging technologies.

• AI & Cloud Technologies: Deep understanding of cloud architectures (AWS preferred), Generative AI, Agentic AI, RAG architectures, and autonomous agents.

• Customer Engagement: Proven ability to engage with customer executives and technical stakeholders to understand needs and drive solutions.

• Communication & Presentation: Excellent written and verbal communication skills; ability to present complex technical information clearly to both technical and non-technical audiences, including executives.

• Methodology Application: Proficiency in applying frameworks like Amazon's Working Backwards methodology and understanding of Agile/Scrum principles.

• Problem-Solving: Strong analytical and problem-solving skills, with the ability to navigate ambiguous situations and develop innovative solutions.

Preferred Skills:

• Certifications: PMP, SCRUM Master, or SAFe certification.

• Cloud Implementation: Experience implementing cloud services, including migrations and modernization projects.

• Metrics & Reporting: Experience defining Key Performance Indicators (KPIs) and Service Level Agreements (SLAs) for multi-million dollar businesses and reporting to senior leadership.

• Product Management Acumen: Understanding of product lifecycle, responsible AI practices, and production readiness considerations.

• Cross-functional Leadership: Experience leading diverse teams, including engineers, designers, and sales representatives.

📝 Enhancement Note: The preferred qualifications, particularly PMP/Scrum/SAFe certifications and experience with cloud migrations, indicate a desire for candidates who can bring structured project management discipline to the agile and innovative nature of AI prototyping. The emphasis on "defining KPIs/SLAs used to drive multi-million dollar businesses" suggests that the impact of these prototypes is expected to be significant.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Case Studies of AI Engagements: Demonstrable examples of leading or significantly contributing to customer-facing engagements, ideally involving AI or complex technical solutions. These should highlight the problem, your approach, the technologies used, and the measurable customer outcomes.

• Process Improvement Examples: Evidence of developing and implementing processes that improved team efficiency, delivery speed, or knowledge sharing within a technical or project management context.

• Methodology Application: Showcase instances where you applied structured methodologies (e.g., Working Backwards, Agile, TPM frameworks) to drive project success.

• Stakeholder Management Artifacts: Examples of communication plans, executive summaries, or operational review materials that demonstrate your ability to manage and report to senior stakeholders.

• Risk Mitigation Plans: Documentation or descriptions of how you proactively identified and managed risks in technical projects.

Process Documentation:

• Workflow Design & Optimization: Examples of how you have mapped, analyzed, and optimized technical workflows, particularly in fast-paced, iterative environments.

• Implementation & Automation: Evidence of coordinating the implementation of technical solutions and leveraging tools or automation to accelerate delivery.

• Measurement & Performance Analysis: Demonstrations of how you establish metrics, track performance, and analyze results to drive continuous improvement and report on business impact.

📝 Enhancement Note: For a role focused on prototyping and customer engineering, the portfolio should emphasize speed, innovation, and measurable impact. Candidates should be prepared to walk through specific examples of how they've navigated ambiguity, facilitated technical teams, and translated customer needs into tangible technical prototypes. The "Working Backwards" methodology should be a recurring theme in portfolio examples.

💵 Compensation & Benefits

Salary Range:

• Boston, MA: $153,600 - $207,800 USD annually

• New York, NY: $169,000 - $228,600 USD annually

• Dallas, TX: $153,600 - $207,800 USD annually

• Seattle, WA: $153,600 - $207,800 USD annually

Benefits:

• Comprehensive Health Insurance: Medical, Dental, Vision, Prescription coverage.

• Life and Disability Insurance: Basic Life & AD&D insurance, with options for supplemental life plans.

• Employee Assistance Program (EAP) & Mental Health Support: Resources for personal and professional well-being.

• Medical Advice Line: Access to healthcare guidance.

• Flexible Spending Accounts (FSAs): For healthcare and dependent care expenses.

• Family Support: Adoption and Surrogacy Reimbursement coverage.

• Retirement Savings: 401(k) matching program.

• Paid Time Off: Generous paid time off policies.

• Parental Leave: Support for new parents.

• Equity: Restricted Stock Units (RSUs) as part of the compensation package.

Working Hours:

• Standard 40 hours per week, with flexibility expected for customer-facing roles and project deadlines, particularly in a hybrid environment. Occasional travel (approx. 25% domestic) may require adjustments to the work schedule.

📝 Enhancement Note: The salary ranges provided are specific to each listed city, reflecting local market variations. The inclusion of RSU's indicates a performance-based component to the compensation, aligning with Amazon's culture. The benefits package is extensive, covering health, wellness, financial planning, and family support, which are strong draws for experienced professionals. The mention of 25% domestic travel suggests a need for flexibility in working hours.

🎯 Team & Company Context

🏢 Company Culture

Industry: Cloud Computing & Technology Services (specifically Amazon Web Services - AWS). Amazon is a global leader in e-commerce and cloud computing, known for its customer obsession, innovation, and scale.

Company Size: Extremely Large (Over 1 million employees globally). This scale means ample resources, complex organizational structures, and opportunities for impact across vast customer bases.

Founded: 1994 (Amazon.com), 2006 (AWS). This long history signifies stability, continuous evolution, and deep expertise in its respective markets.

Team Structure:

• PACE Team: The Prototyping and Customer Engineering (PACE) team is described as a group of "hands-on builders" operating at the "frontier of emerging technology," with deep expertise in Generative AI, Agentic AI, AI/ML, and modern agentic application development.

• Reporting: While not explicitly stated, Customer Solutions Managers typically report into a broader Solutions Architecture, Customer Engineering, or Program Management leadership structure within AWS. This role will likely collaborate closely with Sales (Account Managers, Solutions Architects) and engineering teams.

• Cross-functional Collaboration: High degree of collaboration is expected with Prototyping Engineers, Design Technologists, Global Sales teams, Solutions Architecture teams, customer technical teams (data scientists, engineers), and AWS leadership.

Methodology:

• Customer Obsession & Working Backwards: The core Amazonian principle, starting with the customer and working backward to innovate. This is explicitly stated as a requirement for defining solution requirements.

• Rapid Prototyping: The PACE team's approach involves "rapid, time-boxed prototyping engagements" to demonstrate business value and explore "the art of the possible."

• Data-Driven Decisions: Implicit in an AWS environment, with an expectation to establish metrics, track KPIs, and demonstrate business impact.

• Innovation & Experimentation: The team operates at the "frontier of emerging technology," encouraging exploration and validation of new approaches.

Company Website: https://www.amazon.com/, https://aws.amazon.com/

📝 Enhancement Note: Amazon's culture is characterized by its "Leadership Principles," which are critical for understanding how decisions are made and how employees are expected to behave. While not listed here, candidates should be aware of principles like Customer Obsession, Ownership, Bias for Action, and Dive Deep. The PACE team's focus on cutting-edge AI and rapid prototyping suggests a dynamic, fast-paced environment within a very large organization.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned at a Mid-to-Senior level within the technical program and customer solutions domain. It requires significant experience in leading complex programs and engaging with senior customer stakeholders, indicating a level of autonomy and strategic influence. It's not a junior or entry-level role, nor is it likely a senior director or VP level.

Reporting Structure: The Customer Solutions Manager will report to a manager within the Prototyping and Customer Engineering (PACE) organization, which itself is part of the broader AWS field organization. This structure allows for direct engagement with sales and customer-facing teams while maintaining a technical specialization.

Operations Impact: The impact of this role is directly tied to accelerating customer adoption of advanced AI technologies on AWS. By de-risking innovation through rapid prototyping and demonstrating tangible business value, this role directly influences customer success, revenue growth for AWS AI services, and the overall perception of AWS as an AI leader. The "patterns that influence the entire industry" suggest a significant potential for broader market impact.

Growth Opportunities:

• Specialization: Deepen expertise in specific AI domains (e.g., Agentic AI, LLMs, Responsible AI) and become a recognized subject matter expert within AWS.

• Leadership: Transition into management roles within PACE, leading larger teams or broader programs, or move into senior Solutions Architect or Technical Program Manager roles in other AWS service areas.

• Strategic Influence: Contribute to AWS AI product strategy by providing direct customer feedback and insights from the field.

• Industry Recognition: Develop solutions and patterns that gain industry recognition and can be shared through AWS channels (blogs, re:Invent, etc.).

• Broader AWS Roles: Leverage experience gained at AWS to move into more senior technical or strategic roles within other AWS business units or even other technology companies.

📝 Enhancement Note: The role offers a unique opportunity to be at the forefront of AI adoption with a major cloud provider. Growth potential is strong, emphasizing both technical mastery and strategic program leadership, aligning with typical career paths for highly skilled technical professionals in the cloud space.

🌐 Work Environment

Office Type: Hybrid. This role is based in major US tech hubs (Boston, New York, Dallas, Seattle) and requires a mix of remote work and in-office collaboration. Amazon's hybrid model typically involves a few days in the office per week, facilitating team interaction, brainstorming, and client meetings.

Office Location(s): The job is open in Boston, MA; New York, NY; Dallas, TX; and Seattle, WA. These are major metropolitan areas with significant tech presence, offering access to talent and resources.

Workspace Context:

• Collaborative Environment: The hybrid model and team structure encourage collaboration through in-person meetings, virtual collaboration tools, and shared project spaces. The PACE team's "hands-on builders" ethos suggests an environment where experimentation and peer review are common.

• Tools & Technology: Access to Amazon's extensive cloud infrastructure (AWS), development tools, and communication platforms. Expect to work with cutting-edge AI services and development environments.

• Team Interaction: Regular interaction with Prototyping Engineers, Design Technologists, Sales, Solutions Architects, and customer teams. This provides diverse perspectives and opportunities for cross-pollination of ideas.

Work Schedule: While a standard 40-hour work week is mentioned, the nature of customer engagements, especially in prototyping and dealing with emerging technologies, often requires flexibility. This may involve adjusted hours to accommodate customer time zones, urgent issue resolution, or intense project phases. The 25% domestic travel requirement will also influence the weekly schedule.

📝 Enhancement Note: The hybrid nature implies a need for strong self-management and communication skills. Candidates should be comfortable with both independent work and collaborative sessions, whether in person or virtual. The travel component requires adaptability and willingness to operate outside a fixed office schedule at times.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter will likely conduct an initial phone screen to assess basic qualifications, experience alignment, and interest.

• Hiring Manager Interview: A discussion with the hiring manager to delve deeper into experience, motivations, and fit for the PACE team's objectives. This may include behavioral questions and scenario-based questions related to program management and customer engagement.

• Technical/Team Interviews: Multiple sessions with other team members (Prototyping Engineers, Design Technologists, fellow CSMs, Solutions Architects). These interviews will focus on technical depth in AI and cloud, problem-solving skills, and collaboration style. Expect to discuss past projects in detail.

• Portfolio Presentation: A key component will likely involve presenting 1-2 case studies from your portfolio, demonstrating your approach to customer engagements, technical problem-solving, and impact.

• Leadership Principles Interview: A dedicated session, or integrated throughout, assessing how you embody Amazon's Leadership Principles. This often involves the "Tell me about a time when..." (STAR method) format.

• Final Interview: Potentially with a senior leader or director to confirm fit and discuss the role's strategic impact.

Portfolio Review Tips:

• Focus on Impact: For each case study, clearly articulate the customer's problem, your specific role and actions, the technologies and methodologies used, and most importantly, the measurable business outcomes or value delivered. Quantify results whenever possible.

• Highlight AI/Tech Acumen: Showcase your understanding of AI concepts (GenAI, Agentic AI, RAG) and cloud architectures. Explain how you translated complex technical possibilities into customer solutions.

• Demonstrate Process & Methodology: Illustrate how you applied structured approaches like Working Backwards, Agile, or TPM principles to manage engagements and drive efficiency.

• Client Management Skills: Provide examples of how you built rapport, managed expectations, and communicated effectively with customer executives and technical teams.

• Conciseness & Clarity: Prepare a presentation that is clear, concise, and engaging. Be ready to discuss your projects in detail and answer probing questions. Aim for 3-5 slides per case study.

Challenge Preparation:

• Scenario-Based Questions: Be prepared for questions asking how you would handle specific situations, e.g., "A customer wants to build an agentic AI solution but lacks the necessary data infrastructure. How would you approach this engagement?"

• Problem-Solving Exercises: You might be asked to quickly outline a plan for a hypothetical AI prototyping engagement, considering scope, resources, risks, and success metrics.

• Leadership Principles: Practice articulating your experiences using the STAR method (Situation, Task, Action, Result) for each of Amazon's core Leadership Principles. This is crucial for Amazon interviews.

• AI Trends: Stay updated on current trends and best practices in Generative AI, Agentic AI, and cloud computing.

📝 Enhancement Note: Amazon interviews are known for their rigor, particularly the focus on Leadership Principles and the STAR method. The portfolio presentation is a critical element for this role, serving as tangible proof of capabilities. Candidates should tailor their examples to specifically highlight experience with AI, complex technical programs, and customer-facing consulting.

🛠 Tools & Technology Stack

Primary Tools:

• Cloud Platform: AWS (Amazon Web Services) – deep familiarity is essential, including core services like EC2, S3, Lambda, SageMaker, Bedrock, and others relevant to AI/ML.

• AI/ML Services: Specific experience with Generative AI services (e.g., Amazon Bedrock, LLMs), RAG architectures, agent frameworks, and AI/ML development platforms.

• Project Management Software: Tools like Jira, Confluence, Asana, or similar for tracking tasks, workflows, and documentation.

• Collaboration Suites: Microsoft 365 (Teams, SharePoint) or Google Workspace for communication, documents, and meetings.

Analytics & Reporting:

• AWS Analytics Services: Familiarity with services like Amazon QuickSight, CloudWatch, or other tools for monitoring and analyzing performance metrics.

• Data Visualization Tools: Experience with tools to create dashboards and reports for executive stakeholders, demonstrating AI outcomes and business impact.

• Spreadsheet Software: Advanced proficiency in Excel or Google Sheets for data analysis and reporting.

CRM & Automation:

• CRM Systems: While not explicitly mentioned as a primary tool for this role, understanding CRM principles (e.g., Salesforce) is beneficial for context in customer engagements.

• Automation Tools: Awareness of how automation can be applied to development workflows, testing, and reporting processes, especially within the context of AI-driven development.

• Integration Concepts: Understanding of how different systems and services can be integrated to form cohesive solutions, particularly relevant for complex AI architectures.

📝 Enhancement Note: The emphasis on AWS and specific AI services like Bedrock and LLMs is paramount. Candidates should be prepared to discuss their hands-on experience with these technologies and how they've leveraged them to build prototypes and solutions. Familiarity with Agile project management tools like Jira and Confluence is also highly relevant for tracking iterative development cycles.

👥 Team Culture & Values

Operations Values:

• Customer Obsession: A fundamental Amazon principle. Every action and decision should start with understanding and meeting customer needs. For this role, it means deeply understanding customer pain points and desired outcomes for AI solutions.

• Ownership: Taking full responsibility for engagements, from ideation to delivery, and proactively addressing challenges.

• Bias for Action: Moving quickly and decisively, especially in a prototyping context. Embracing calculated risks to accelerate learning and delivery.

• Dive Deep: Thoroughly understanding the intricacies of AI technologies, customer business problems, and technical architectures.

• Invent and Simplify: Creating innovative solutions and finding ways to make complex challenges more manageable for customers and the team.

• Earn Trust: Building strong relationships with customers and AWS leadership through reliable execution and transparent communication.

Collaboration Style:

• Cross-functional Integration: The PACE team thrives on close collaboration between engineering, design, sales, and customer teams. Expect a highly collaborative environment where ideas are shared freely.

• Process Review & Feedback: A culture of continuous improvement, where team members provide and receive constructive feedback to refine processes and enhance prototype quality and speed.

• Knowledge Sharing: Emphasis on sharing learnings, best practices, and insights gained from diverse customer engagements across the PACE community to foster collective growth and expertise.

• Hands-on & Pragmatic: The team's "builders" mentality suggests a pragmatic approach, focusing on tangible results and practical application of technology.

📝 Enhancement Note: Understanding and demonstrating alignment with Amazon's Leadership Principles is critical for success in this role and during the interview process. The team culture emphasizes innovation, speed, and customer focus, creating a dynamic and challenging work environment.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapidly Evolving AI Landscape: Staying abreast of the constant advancements in AI, Generative AI, and Agentic AI requires continuous learning and adaptation.

• Ambiguous Customer Problems: Working on "problems that don't have obvious answers" requires strong analytical skills, creativity, and resilience.

• Balancing Speed and Quality: The need for rapid prototyping must be balanced with ensuring responsible AI practices, scalability considerations, and high-quality deliverables.

• Cross-functional Coordination: Orchestrating efforts across multiple internal teams (Sales, SA, PACE) and diverse customer stakeholders can be complex.

• Demonstrating Tangible ROI: Clearly articulating and proving the business value and ROI of experimental AI prototypes to customers and leadership.

Learning & Development Opportunities:

• Deep AI Specialization: Gain unparalleled expertise in cutting-edge AI technologies and their application within enterprise contexts via AWS services.

• Industry Conferences & Certifications: Opportunities to attend major AWS events (like re:Invent) and pursue relevant certifications in AI, cloud, or program management.

• Mentorship & Leadership Development: Access to mentorship programs and leadership training within Amazon, fostering career progression.

• Exposure to Diverse Industries: Work with a wide range of customers across various industries, providing broad business and technical exposure.

• Influence Industry Best Practices: Contribute to developing new patterns and approaches in AI adoption that can shape industry standards.

📝 Enhancement Note: This role is ideal for individuals who thrive on technical challenges, enjoy working at the forefront of innovation, and are motivated by helping customers solve complex problems. The challenges are significant but offer commensurate opportunities for professional growth and impact.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you led a complex technical program with ambiguous requirements. What was your approach, and what was the outcome?" (Focus on methodology, risk management, and stakeholder communication).

• "How would you apply Amazon's Working Backwards methodology to a customer looking to build an AI-powered customer service chatbot?" (Demonstrate understanding of the framework and its application to AI).

• "Imagine a customer is hesitant to adopt Generative AI due to concerns about responsible AI. How would you address their concerns and build trust?" (Highlight your approach to responsible AI and risk mitigation).

Company & Culture Questions:

• "Why Amazon and specifically why the PACE team?" (Research Amazon's culture, leadership principles, and PACE's mission).

• "Tell me about a time you had to influence a senior executive on a technical strategy. What was the situation, and how did you approach it?" (STAR method, focus on communication and persuasion).

• "How do you handle conflicting priorities or urgent requests from different stakeholders?" (Demonstrate prioritization and communication skills).

Portfolio Presentation Strategy:

• Select Relevant Cases: Choose 1-2 case studies that best showcase your experience with AI, complex technical programs, customer engagements, and successful outcomes.

• Structure with STAR: For each project, clearly outline the Situation, Task, Action, and Result. Emphasize your specific contributions and the measurable impact.

• Visualize Data: Use clear visuals (diagrams, charts, dashboards) to illustrate technical architectures, processes, and key performance metrics.

• Focus on "How" and "Why": Beyond just describing what you did, explain how you approached the problem and why you made certain decisions.

• Practice Delivery: Rehearse your presentation to ensure it's concise, engaging, and within the allotted time. Be prepared for in-depth Q&A.

📝 Enhancement Note: Amazon interviews are heavily weighted towards behavioral questions based on their Leadership Principles. Candidates must be prepared to provide specific, detailed examples using the STAR method. The portfolio presentation is a critical opportunity to demonstrate practical application of skills and knowledge.

📌 Application Steps

To apply for this Customer Solutions Manager position at Amazon:

• Submit Your Application: Utilize the official Amazon Jobs portal link provided. Ensure your resume is up-to-date and tailored to the role's requirements.

• Customize Your Resume: Highlight keywords related to AI, Generative AI, Agentic AI, Cloud Architecture (AWS), Program Management, Customer Engagement, Working Backwards methodology, and any relevant certifications (PMP, Scrum). Quantify achievements wherever possible with metrics.

• Prepare Your Portfolio: Curate 1-2 strong case studies demonstrating your experience in leading technical/AI programs, customer engagement, process improvement, and delivering measurable business impact. Prepare a concise presentation deck for these case studies.

• Practice Leadership Principles: Study Amazon's Leadership Principles and prepare specific examples using the STAR method for each principle. These are heavily scrutinized during interviews.

• Research Amazon & PACE: Understand Amazon's business, AWS's role, and the specific mission and work of the PACE team. Familiarize yourself with their approach to AI and prototyping.

• Anticipate Technical/Scenario Questions: Be ready to discuss your experience with AI technologies, cloud services, and how you would approach common challenges in this role.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.