Customer Solutions Manager, Prototyping & Customer Engineering

📍 Job Overview

Job Title: Customer Solutions Manager, Prototyping & Customer Engineering Company: Amazon Location: London, England, United Kingdom Job Type: Full-time Category: Customer Engineering / Technical Program Management (AI Focus) Date Posted: May 04, 2026 Experience Level: Mid-Senior Level (Implied 5-10 years) Remote Status: On-site

🚀 Role Summary

• Lead strategic, end-to-end AI-focused customer engagements, from initial concept to final delivery, leveraging Amazon's Working Backwards methodology.
• Orchestrate and guide Prototyping Engineers and Design Technologists in designing innovative AI solutions, including large language models, RAG architectures, and autonomous agents.
• Drive the adoption of AI-driven development tools to accelerate the delivery of high-quality prototypes and proof-of-concepts for enterprise clients.
• Collaborate closely with Global Sales and Solutions Architecture teams to identify and qualify new Generative AI and Agentic AI opportunities, contributing to program-level strategy.

📝 Enhancement Note: This role sits within Amazon's Prototyping and Customer Engineering (PACE) team, indicating a strong focus on hands-on technical problem-solving and rapid innovation for AWS customers. The emphasis on "builder's mentality" and "show me" over "tell me" suggests a preference for pragmatic, results-oriented individuals comfortable with ambiguity and emerging technologies. The "Customer Solutions Manager" title, coupled with "Prototyping & Customer Engineering," points to a hybrid role blending technical program management, customer advocacy, and deep engagement with cutting-edge AI technologies.

📈 Primary Responsibilities

• Own the full lifecycle of strategic AI-focused customer engagements, from kick-off through delivery, ensuring alignment with customer outcomes via the Working Backwards methodology.
• Facilitate Prototyping Engineers and Design Technologists in AI solution design, specifically focusing on agentic architecture patterns and the efficient coordination of AI-driven development tools.
• Partner with Global Sales and Solutions Architecture teams to identify, qualify, and develop Generative AI and Agentic AI opportunities, providing program-level strategic input to PACE leadership.
• Cultivate and maintain strong relationships with customer executives and AWS leadership through structured communications, regular operational reviews, and transparent reporting on AI outcomes and tangible business impact.
• Establish key engagement metrics, diligently track delivery velocity, and contribute to program-level Key Performance Indicators (KPIs) that accurately measure success and value realization.
• Develop and implement robust processes to enhance team efficiency, accelerate prototype delivery speed, and foster knowledge sharing across the broader PACE community.
• Proactively identify and assess risks inherent in AI implementations, developing and executing effective mitigation strategies to ensure successful and impactful outcomes.
• Apply strong product thinking principles to ensure AI prototypes are designed with a focus on responsible AI practices, production readiness, and sustained long-term customer value.

📝 Enhancement Note: The responsibilities highlight a blend of strategic program management, technical leadership in AI, and customer relationship management. The emphasis on "agentic architecture patterns," "AI-driven development tools," and "Responsible AI" indicates a need for deep understanding of current AI trends and best practices. The requirement to "develop and implement processes" points to a continuous improvement mindset crucial for operations roles.

🎓 Skills & Qualifications

Education:

• Bachelor's degree in a relevant field such as Science, Technology, Engineering, Mathematics, Business, or an equivalent practical experience.
• Preference for candidates holding professional certifications like PMP, SCRUM, or SAFe, demonstrating a commitment to structured project management methodologies.

Experience:

• Proven track record in leading large-scale, technical or engineering programs from inception to completion, with demonstrable thought leadership, robust business case development, and a focus on realizing customer benefits.
• Extensive experience in customer-facing roles, effectively engaging with customer executives, technologists, and partners to solve complex business problems using advanced technologies.
• Demonstrated ability to lead diverse technical and non-technical transformation project teams, fostering collaboration across broad functional areas.
• Experience in defining and tracking Key Performance Indicators (KPIs) and Service Level Agreements (SLAs) that drive multi-million dollar businesses and reporting these metrics effectively to senior leadership.

Required Skills:

• AI Solution Design & Architecture: Ability to conceptualize and design AI-powered solutions, including expertise in Generative AI and Agentic AI patterns.
• Program Management: Proven experience leading complex, technical programs with a strong emphasis on planning, execution, risk management, and stakeholder communication.
• Customer Engagement & Relationship Management: Skill in building and maintaining strong relationships with executive-level clients and internal stakeholders.
• Technical Acumen: Solid understanding of cloud architectures (AWS preferred), emerging technologies, and the ability to quickly grasp new technical concepts.
• Working Backwards Methodology: Practical application of Amazon's customer-centric approach to problem-solving and solution development.
• Risk Mitigation: Proactive identification and strategic planning to address potential risks in technical projects, especially within AI.
• Stakeholder Management: Effective communication and negotiation skills to manage expectations and align diverse groups towards common goals.
• KPI & Metrics Driven: Ability to define, track, and report on key performance indicators to measure project success and business impact.
• Process Improvement: Experience in developing and implementing processes to enhance team efficiency and delivery speed.

Preferred Skills:

• Cloud Services Implementation: Experience with cloud service implementation, including migration and modernization projects.
• Agile Methodologies: Certification or practical experience with SCRUM, Agile, or SAFe frameworks.
• Product Thinking: Ability to approach AI prototypes from a product management perspective, considering production readiness and long-term value.
• Data Science Collaboration: Familiarity with working alongside data science teams to optimize AI models and solutions.
• Agent Orchestration: Deep understanding of agent orchestration patterns and their application in complex systems.

📝 Enhancement Note: The qualifications emphasize a strong technical foundation in AI and cloud computing, coupled with robust program management and customer-facing skills. The distinction between "required" and "preferred" skills suggests a candidate profile that is both technically proficient and a skilled communicator and leader. The mention of PMP, SCRUM, and SAFe certifications points towards a preference for structured, agile project management approaches within this technically advanced role.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Case Studies of AI Engagements: Detailed documentation of at least 2-3 complex AI-focused customer engagements led by the candidate, showcasing the application of the Working Backwards methodology from problem identification to solution delivery.
• Process Optimization Examples: Evidence of developing and implementing new processes or improving existing workflows that demonstrably increased team efficiency, prototype delivery speed, or improved quality in technical projects.
• System Implementation & Integration: Examples illustrating how the candidate has overseen the implementation or integration of technical solutions, particularly those involving cloud services or new technology adoption.
• ROI & Business Impact Demonstration: Quantifiable results from past projects, clearly articulating the business value, customer benefits, and return on investment (ROI) achieved through the implemented solutions.

Process Documentation:

• Workflow Design & Optimization: Showcase examples of documented workflows for managing complex technical projects, highlighting how these were designed for clarity, efficiency, and adaptability.
• Implementation & Automation Methods: Provide insights into the methodologies used for implementing technical solutions, with an emphasis on how automation was leveraged to accelerate delivery or enhance outcomes.
• Measurement & Performance Analysis: Demonstrate experience in defining metrics, tracking performance, and conducting analysis to evaluate the success of technical initiatives and inform future improvements.

📝 Enhancement Note: For a role like this, the portfolio should not just list past projects but tell a story of impact. Candidates should be prepared to walk through a specific AI prototype development process, detailing challenges, decisions made, and how metrics were used to guide the project. Emphasis should be placed on how the candidate "derisked" or "accelerated" the launch of new products/services for customers.

💵 Compensation & Benefits

Salary Range:

• Based on industry benchmarks for Customer Solutions Managers or Technical Program Managers with specialized AI expertise in London, a salary range of £80,000 - £120,000 per annum is estimated. This estimate considers the mid-senior experience level, the high-demand nature of AI expertise, and the cost of living in London.

Benefits:

• Comprehensive health, dental, and vision insurance plans.
• Generous paid time off (PTO), including vacation, sick leave, and public holidays.
• Retirement savings plan with company matching contributions (e.g., 401k equivalent).
• Employee Stock Purchase Plan (ESPP) for Amazon shares.
• Access to Amazon's extensive learning and development resources, including training on cutting-edge AI technologies and cloud services.
• Employee discounts on Amazon products and services.
• Relocation assistance may be available for qualified candidates.
• Opportunities for professional development and certifications (e.g., PMP, cloud certifications).

Working Hours:

• Standard full-time working hours are typically 40 hours per week. However, given the nature of customer-facing roles and project deadlines, flexibility may be required, with potential for occasional evening or weekend work to meet critical project milestones or customer needs.

📝 Enhancement Note: The salary range is an estimate based on publicly available data for similar roles in London and the specified experience level. Actual compensation will be determined by factors such as candidate experience, specific skills, and internal Amazon compensation bands. The benefits package is typical for a large tech company like Amazon, with a strong emphasis on employee well-being, long-term financial security, and continuous learning.

🎯 Team & Company Context

🏢 Company Culture

Industry: E-commerce, Cloud Computing, Artificial Intelligence, Digital Streaming, Consumer Electronics. Amazon operates across a vast spectrum of technology-driven industries, making it a dynamic environment for operations and engineering professionals. Company Size: Amazon is a global enterprise with hundreds of thousands of employees worldwide, indicating a large, complex organizational structure with numerous specialized teams and extensive resources. Founded: Founded in 1994 by Jeff Bezos, Amazon has grown from an online bookstore to a global technology powerhouse, known for its customer obsession, innovation, and rapid expansion into new markets and technologies.

Team Structure:

• The Prototyping and Customer Engineering (PACE) team is a specialized group within AWS, focused on hands-on customer engagements. It operates with a high degree of autonomy and technical depth.
• The PACE team likely comprises Prototyping Engineers, Design Technologists, and technical managers, working collaboratively to deliver rapid, time-boxed solutions for AWS customers.
• This role involves significant cross-functional collaboration with AWS Global Sales, Solutions Architects, product teams, and customer-side technical and business stakeholders.

Methodology:

• Working Backwards: A core Amazon methodology where customer needs and desired outcomes are defined first, guiding all subsequent development and problem-solving.
• Data-Driven Decision Making: Emphasis on using metrics, KPIs, and performance data to inform strategy, measure success, and drive continuous improvement.
• Agile & Iterative Development: For prototyping and rapid solution development, embracing iterative cycles, feedback loops, and flexibility to adapt to emerging findings.
• Innovation & Experimentation: A culture that encourages exploring new technologies, experimenting with novel approaches, and pushing the boundaries of what's possible.

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

📝 Enhancement Note: Amazon's culture is characterized by its intense focus on the customer, a high degree of "bias for action," and a willingness to invent and pioneer. The PACE team, specifically, is likely a high-performance unit operating at the cutting edge of AI and cloud technologies, demanding both technical excellence and strong interpersonal skills.

📈 Career & Growth Analysis

Operations Career Level: This role represents a mid-to-senior level position within the technical program management and customer engineering domain, specifically focused on AI. It's a step beyond standard Technical Program Manager roles, requiring a proactive, "builder's mentality" and direct engagement with nascent technologies and ambiguous problems. The scope involves end-to-end ownership of customer engagements, influencing strategic customer adoption of AI.

Reporting Structure: The Customer Solutions Manager will likely report into a PACE leadership role, such as a Director or Senior Manager of Prototyping & Customer Engineering. They will work closely with and influence peer managers in Sales, Solutions Architecture, and other engineering teams.

Operations Impact: The primary impact of this role is enabling AWS customers to successfully adopt and leverage advanced AI technologies, thereby accelerating their innovation, improving their business outcomes, and strengthening their commitment to the AWS platform. By derisking AI adoption, the PACE team directly contributes to customer success and, by extension, AWS revenue growth and market leadership in AI.

Growth Opportunities:

• Specialization in AI: Deepen expertise in specific AI domains like Generative AI, Agentic AI, LLMs, and related architectures, becoming a recognized subject matter expert.
• Leadership Progression: Advance into senior management roles within the PACE organization or transition to broader program leadership roles within AWS.
• Product Development: Potentially move into product management or product strategy roles focused on AI services or customer-facing solutions.
• Cross-Functional Mobility: Leverage experience to move into strategic roles within Sales, Solutions Architecture, or specialized technical consulting teams across AWS.
• Industry Influence: Contribute to industry best practices and thought leadership in AI adoption and customer engineering through speaking engagements, publications, or community contributions.

📝 Enhancement Note: This role offers a clear path for growth for individuals passionate about AI and customer success. The emphasis on emerging technologies and direct customer impact provides significant learning opportunities and visibility. The ability to shape customer adoption patterns for AI positions this role as critical to AWS's future growth.

🌐 Work Environment

Office Type: This is an on-site role, indicating a traditional office environment where in-person collaboration, team meetings, and direct interaction with colleagues and customers are expected. Office Location(s): London, England. Amazon operates large corporate offices in London, likely offering modern facilities designed for collaboration and innovation, equipped with the necessary technology infrastructure.

Workspace Context:

• Collaborative Environment: The office space is expected to facilitate close collaboration among Prototyping Engineers, Design Technologists, Sales teams, and Solutions Architects, fostering a dynamic exchange of ideas essential for rapid prototyping.
• Technology-Rich: Access to high-performance computing resources, advanced development tools, and robust network infrastructure will be standard, supporting the demanding nature of AI development and customer engagements.
• Customer Interaction Hub: The London office likely serves as a key hub for engaging with European clients, offering meeting rooms and facilities conducive to client presentations, workshops, and strategic discussions.

Work Schedule: While the standard is 40 hours per week, the nature of customer engagements, especially those involving cutting-edge technology and global clients, may necessitate flexibility. This could include occasional extended hours to meet deadlines, participate in international calls across different time zones, or support critical customer events.

📝 Enhancement Note: As an on-site role in a major tech hub like London, candidates can expect a professional, fast-paced environment. The emphasis on customer engagements suggests that team members will regularly interface with clients, requiring strong presentation and communication skills within a collaborative office setting.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will conduct an initial call to assess basic qualifications, understand career motivations, and gauge cultural fit.
• Technical Phone Screen: A deeper dive into technical experience, particularly AI concepts, cloud architecture, and program management methodologies. May involve problem-solving questions.
• Hiring Manager Interview: Focus on leadership, strategic thinking, customer engagement experience, and alignment with Amazon's leadership principles.
• Bar Raiser Interview(s): These interviews are conducted by senior Amazonians from different teams and are designed to ensure candidates meet Amazon's high bar for talent. They will focus on behavioral questions related to Amazon's Leadership Principles and specific role competencies.
• Portfolio Review & Case Study Presentation: Candidates will likely be asked to present 1-2 detailed case studies from their portfolio, showcasing a complex AI engagement, their role, challenges, solutions, and outcomes. This is a critical component.
• Final Round/Debrief: A final discussion to consolidate feedback and make a hiring decision.

Portfolio Review Tips:

• Focus on Impact: For each case study, clearly articulate the customer's problem, your specific role and contributions, the AI technologies used, the challenges faced (technical, customer, process), the solutions implemented, and the quantifiable business outcomes achieved.
• Highlight Process: Demonstrate how you applied methodologies like Working Backwards, Agile, or specific process improvement frameworks. Detail your approach to risk mitigation, stakeholder management, and communication.
• Technical Depth: Be prepared to discuss the technical aspects of the AI solutions in detail, including architecture, data considerations, and implementation trade-offs.
• Customer-Centricity: Emphasize how your work directly addressed customer needs and delivered tangible value.
• Conciseness and Clarity: Structure your presentation logically and deliver it in a clear, concise manner, respecting the allocated time.

Challenge Preparation:

• Leadership Principles: Thoroughly study and prepare examples for each of Amazon's Leadership Principles. Behavioral questions will be a significant part of the interview process.
• AI Trends: Stay current on Generative AI, Agentic AI, LLMs, RAG, and responsible AI practices. Be ready to discuss their applications and implications.
• Problem-Solving Scenarios: Practice breaking down complex, ambiguous problems into manageable components and developing logical, data-informed solutions.
• Customer Scenarios: Prepare to discuss how you would handle challenging customer interactions or situations where technical feasibility is uncertain.

📝 Enhancement Note: The interview process at Amazon is rigorous and heavily focused on behavioral questions tied to their Leadership Principles. Candidates must prepare specific, STAR-method (Situation, Task, Action, Result) examples that demonstrate their capabilities in program management, AI, and customer engagement. The portfolio presentation is a crucial element for this specific role.

🛠 Tools & Technology Stack

Primary Tools:

• CRM: Salesforce or similar CRM for managing customer relationships, tracking opportunities, and pipeline management.
• Project Management: Tools like Jira, Confluence, Asana, or internal Amazon equivalents for task tracking, sprint planning, and documentation.
• Collaboration Suites: Microsoft 365 (Teams, SharePoint, Outlook) or Google Workspace for communication, document sharing, and calendaring.
• Diagramming Tools: Lucidchart, Visio, or similar for creating architectural diagrams, workflow charts, and process maps.

Analytics & Reporting:

• AWS Services: Deep familiarity with a wide range of AWS services is essential, including those related to AI/ML (e.g., Amazon SageMaker, Amazon Bedrock), compute (EC2), storage (S3), databases (RDS, DynamoDB), and networking.
• Data Visualization: Tools like Tableau, Power BI, or Amazon QuickSight for creating dashboards and reports to track KPIs and communicate AI outcomes.
• Spreadsheet Software: Advanced proficiency in Excel or Google Sheets for data analysis and reporting.

CRM & Automation:

• AI/ML Platforms: Hands-on experience or strong understanding of platforms like Amazon SageMaker, Hugging Face, or similar for building, training, and deploying machine learning models, including LLMs.
• Agentic AI Frameworks: Familiarity with frameworks and tools for building autonomous agents and orchestration patterns.
• Integration Tools: Understanding of API integration principles and potentially tools like AWS Step Functions or other workflow automation services.

📝 Enhancement Note: Proficiency with AWS services is paramount, given the role's context within Amazon. The emphasis on AI means candidates should be familiar with ML/AI platforms and frameworks. The ability to translate technical capabilities into business value through reporting and dashboards is also key.

👥 Team Culture & Values

Operations Values:

• Customer Obsession: A relentless focus on understanding and meeting customer needs, making customer outcomes the primary driver for all decisions and actions.
• Bias for Action: A proactive approach to problem-solving and decision-making, prioritizing speed and agility while maintaining quality and mitigating risks.
• Invent and Simplify: A drive to innovate, create new solutions, and continuously simplify complex processes and technologies to benefit customers.
• Ownership: Taking full responsibility for engagements, projects, and outcomes, demonstrating accountability and a commitment to delivering results.
• Deliver Results: A focus on achieving measurable outcomes and exceeding expectations, consistently driving for success.

Collaboration Style:

• Cross-Functional Integration: Expectation to work seamlessly with diverse teams (Sales, Engineering, Design, Architecture) to achieve shared goals, fostering open communication and mutual respect.
• Process Review Culture: An environment where processes are regularly evaluated, challenged, and improved to enhance efficiency and effectiveness, with constructive feedback encouraged.
• Knowledge Sharing: A culture that promotes the sharing of insights, best practices, and learnings across teams and projects to elevate collective understanding and capability, especially in rapidly evolving fields like AI.

📝 Enhancement Note: Amazon's culture is deeply ingrained in its Leadership Principles. For operations and customer-facing roles, these principles translate into a highly driven, customer-focused, and results-oriented work environment. Collaboration is essential but must be efficient and aligned with driving concrete outcomes.

⚡ Challenges & Growth Opportunities

Challenges:

• Ambiguity and Novelty: Working with cutting-edge AI technologies and undefined customer problems means facing significant ambiguity and tackling tasks that don't have established solutions. This requires strong analytical skills and comfort with the unknown.
• Pace of Innovation: The AI landscape evolves at an unprecedented speed. Keeping pace with new models, techniques, and tools while delivering customer projects demands continuous learning and adaptation.
• Managing Cross-Functional Dependencies: Successfully orchestrating engagements involves coordinating multiple internal teams and external customer stakeholders, each with their own priorities and constraints.
• Balancing Speed with Quality: The "builder's mentality" and drive for speed must be balanced with ensuring responsible AI practices, production readiness, and long-term customer value, which can be a delicate act.

Learning & Development Opportunities:

• AI Skill Advancement: Direct exposure to and hands-on work with the latest Generative AI, Agentic AI, and LLM technologies provides unparalleled opportunities for skill development.
• Industry Conferences & Certifications: Potential for Amazon to sponsor participation in leading AI and cloud conferences, as well as relevant professional certifications.
• Operations Mentorship: Access to experienced leaders within PACE and AWS who can provide mentorship on program management, customer engagement strategies, and technical leadership.
• Leadership Development: Opportunities to hone leadership skills through managing complex projects, influencing stakeholders, and potentially mentoring junior team members.

📝 Enhancement Note: The challenges in this role are directly tied to its cutting-edge nature. Embracing these challenges as learning opportunities is key to growth. Candidates should be prepared to articulate how they approach ambiguity and rapid technological change.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you led a complex technical program involving emerging technology. What were the biggest challenges, and how did you overcome them?" (Focus on Leadership Principles: Ownership, Deliver Results)
• "How would you approach defining success criteria for an AI prototype engagement with a customer who is unsure of their exact needs?" (Focus on Customer Obsession, Working Backwards)
• "Walk me through your process for identifying and mitigating risks in a project that involves novel AI applications." (Focus on Ownership, Invent and Simplify)

Company & Culture Questions:

• "Why are you interested in working for Amazon and specifically the PACE team?" (Assess alignment with company values and role focus)
• "How do you stay current with the rapidly evolving field of Artificial Intelligence?" (Gauges proactivity and passion for the domain)
• "Describe a situation where you had to influence senior stakeholders who had different technical opinions." (Assess communication and stakeholder management skills)

Portfolio Presentation Strategy:

• Structure: Begin with the customer's problem, your role, the proposed solution, key technical details, your specific actions, challenges encountered, how they were resolved, and the final, quantifiable business impact.
• Metrics Focus: Clearly present any KPIs or metrics used to track progress and measure success. Quantify the business value delivered (e.g., % improvement in efficiency, $ saved, new revenue generated).
• Methodology Explanation: Explain how you managed the engagement, referencing specific methodologies (Working Backwards, Agile, etc.) and your decision-making process.
• Show, Don't Just Tell: Use diagrams, architecture snippets, or simplified workflow representations where appropriate to illustrate complex concepts.
• Q&A Readiness: Anticipate questions about technical feasibility, alternative solutions, scalability, responsible AI considerations, and customer management.

📝 Enhancement Note: Preparation for Amazon interviews must include deep reflection on past experiences through the lens of their Leadership Principles. For this role, demonstrating a tangible impact through portfolio case studies is as important as articulating technical understanding.

📌 Application Steps

To apply for this operations position:

• Submit your application through the Amazon Jobs portal, ensuring your resume highlights relevant experience in technical program management, AI, cloud technologies, and customer-facing roles.
• Portfolio Customization: Curate 2-3 of your most impactful AI-focused customer engagement case studies. For each, clearly define the customer's challenge, your role, the technical solution, the process followed (especially Working Backwards), the challenges overcome, and the quantifiable business results.
• Resume Optimization: Tailor your resume to include keywords from the job description such as "Generative AI," "Agentic AI," "Working Backwards," "Program Management," "Customer Engineering," "AWS," and "Risk Mitigation." Quantify achievements wherever possible.
• Interview Preparation: Practice articulating your experience using the STAR method, focusing on Amazon's Leadership Principles. Prepare to present your portfolio case studies clearly and concisely, and be ready for technical questions on AI and cloud architecture.
• Company Research: Familiarize yourself with Amazon's Leadership Principles, the PACE team's mission, and recent AWS AI announcements. Understand how your experience aligns with Amazon's customer-centric culture and drive for innovation.

⚠️ 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.