Prototyping Architect, CMHK Prototyping and AI Customer Engineering (PACE)

📍 Job Overview

Job Title: Prototyping Architect, CMHK Prototyping and AI Customer Engineering (PACE)

Company: Amazon

Location: Shenzhen, Guangdong Province, China

Job Type: Full-Time

Category: Revenue Operations / Sales Operations / GTM Strategy (AI & Cloud Solutions)

Date Posted: 2026-08-12

Experience Level: 3-5 Years

Remote Status: On-site

🚀 Role Summary

• Architect and build production-ready Generative AI and Agentic AI prototypes directly with customers, leveraging AWS AI services and cloud-native architectures.

• Serve as a trusted technical advisor to customers, guiding them through complex AI adoption strategies, LLM selection, and agent design patterns.

• Utilize AI-driven development tools and modern application patterns to accelerate prototype development, demonstrating the transformative potential of AI.

• Collaborate with cross-functional teams, including Technical Program Managers and Design Technologists, to deliver customer engagements and influence AI product roadmaps.

• Drive adoption of Generative AI and Agentic AI across the AWS customer base through reusable patterns, code libraries, and thought leadership.

📝 Enhancement Note: This role bridges the gap between advanced AI technology and customer business needs, requiring a strong blend of technical expertise in AI/ML, full-stack development, and strategic customer engagement. The focus on rapid prototyping and direct customer interaction aligns it closely with GTM strategy enablement and sales acceleration through technical solutions.

📈 Primary Responsibilities

• Architect and build working Generative AI and Agentic AI prototypes directly with customers using AWS AI services (Bedrock, SageMaker, Kiro/Q Developer) and cloud-native architectures, including autonomous agents, multi-agent systems, RAG architectures, and LLM-powered applications that demonstrate production-ready solutions.

• Leverage AI-driven development tools (Cursor, Kiro, Q Developer, Cline, Windsurf) to accelerate prototype development, implementing patterns like prompt engineering, function calling, agent orchestration, and tool use.

• Serve as a trusted technical advisor to customers on LLM selection, agent design patterns, agentic architectures, and AI adoption strategies, guiding them through complex technical decisions and trade-offs.

• Collaborate with Technical Program Managers and Design Technologists to deliver customer engagements on time, while partnering with AWS service teams to provide feedback and influence AI product roadmaps.

• Create and share reusable patterns and thought leadership through agent frameworks, code libraries, technical content, whitepapers, blogs, and conference presentations that accelerate Generative AI and Agentic AI adoption across the AWS customer base.

📝 Enhancement Note: The responsibilities emphasize hands-on coding and architectural decision-making within customer engagements, indicating a role that directly supports sales and customer success by showcasing tangible AI solutions. The focus on rapid iteration and "prototypes in days and weeks" highlights a GTM approach driven by technical demonstration and proof-of-concept execution.

🎓 Skills & Qualifications

Education: While not explicitly stated, a Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field is typically expected for this level of role.

Experience: 3-5 years of experience in designing, building, refactoring, or operating systems, either on-premises or in the cloud. Experience in developing and deploying LLMs in production on GPUs, Neuron, or TPU is highly valued.

Required Skills:

• Proven experience contributing to the architecture and design (architecture, design patterns, reliability, and scaling) of new and current systems.

• Deep knowledge of software development tools, agile methodologies, active, hands-on coding, and application design experience.

• Proficiency developing in one or more modern programming languages, tools, and frameworks (e.g., Python, Java, R, Scala, Spark, Kafka, Hadoop ecosystem, Presto, Hive, Teradata, Elasticsearch).

• Experience using GenAI development tools (e.g., code assistants/agents, AI-powered IDEs/CLIs) to support software development tasks.

• Experience in written and verbal communication with the ability to present complex technical information in a clear and concise manner to executives and non-technical leaders. Preferred Skills:

• Experience with designing and building applications using AWS services such as Lambda, AWS Elastic Beanstalk, Kubernetes.

• Experience within specific technology domain areas (e.g., software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics).

• Experience implementing cloud services including migrations and modernization projects or similar.

• Experience with iterative, agile development methodologies (including Scrum).

• Experience communicating clearly and concisely with leadership, stakeholders, and cross-functional teams.

• Experience in strategic thinking about business, enterprise software products, and new technology platforms and architectures.

• Experience in creating process improvements with automation and analysis, or experience troubleshooting problems and offering solutions to streamline complex challenges.

📝 Enhancement Note: The requirements highlight a strong foundation in software development and cloud architecture, with a specific emphasis on Generative AI and Agentic AI technologies. The blend of coding proficiency, architectural design, and communication skills is crucial for effectively translating complex AI capabilities into customer-facing solutions that drive adoption.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase of production-ready Generative AI and Agentic AI prototypes built using AWS services (Bedrock, SageMaker).

• Demonstrations of RAG architectures, autonomous agents, multi-agent systems, and LLM-powered applications.

• Examples of prompt engineering, function calling, agent orchestration, and tool use implemented in code.

• Architectural diagrams and design documents illustrating system design, reliability, and scaling principles for AI systems.

• Evidence of leveraging AI-driven development tools to accelerate development cycles. Process Documentation:

• Detailed case studies explaining the architecture and implementation of AI prototypes, including challenges faced and solutions implemented.

• Documentation of reusable patterns and code libraries developed for agent frameworks and LLM integrations.

• Whitepapers, blogs, or presentations detailing thought leadership on Generative AI and Agentic AI adoption strategies.

• Examples of how AI prototypes have directly addressed customer business challenges and demonstrated measurable outcomes.

• Documentation of collaboration with Technical Program Managers and Design Technologists to deliver customer engagements.

📝 Enhancement Note: Candidates are expected to present tangible evidence of their ability to architect and build advanced AI solutions. The portfolio should reflect a deep understanding of AI/ML principles, cloud-native development, and the practical application of these technologies in customer-facing scenarios, emphasizing speed and impact.

💵 Compensation & Benefits

Salary Range: Based on industry benchmarks for similar roles in Shenzhen, China, and considering Amazon's compensation structure for technical architects with 3-5 years of experience in specialized AI fields, a competitive annual salary range would likely fall between ¥400,000 - ¥700,000 RMB. This estimate considers the high demand for AI expertise, the cost of living in Shenzhen, and Amazon's typical compensation for engineering roles.

Benefits:

• Mentorship from experienced AI and cloud architects.

• Opportunities for continuous learning and career advancement within AWS.

• Inclusive team culture that values diverse experiences and perspectives.

• Work-life harmony initiatives and a flexible working culture.

• Access to cutting-edge AI technologies and development tools.

• Comprehensive health and welfare benefits (specifics to be confirmed by Amazon).

• Potential for stock awards and performance-based bonuses.

Working Hours: Standard full-time work week, typically around 40 hours, with potential for extended hours based on customer engagement deadlines and project needs. Travel is expected, generally around 25%.

📝 Enhancement Note: The salary estimate is based on publicly available data for senior software engineering and architect roles in major tech hubs in China, specifically Shenzhen, and adjusted for the specialized AI focus and the reputation of Amazon. The benefits listed are directly extracted from the provided text and are common for large technology organizations.

🎯 Team & Company Context

🏢 Company Culture

Industry: Cloud Computing, Artificial Intelligence, E-commerce, Technology Services. Amazon Web Services (AWS) operates at the forefront of cloud infrastructure and AI innovation, serving a vast global customer base.

Company Size: AWS is a division of Amazon, a multinational technology conglomerate with over 1.5 million employees worldwide. AWS itself employs tens of thousands of individuals globally, indicating a large and complex organizational structure.

Founded: AWS was launched in 2006, building upon Amazon's internal infrastructure expertise. The AWS China Regions were launched in 2016, demonstrating a significant commitment to the Chinese market.

Team Structure:

• The PACE (Prototyping and AI Customer Engineering) team is a specialized unit within AWS Global Sales, focused on accelerating customer innovation through AI.

• The team comprises Prototyping Architects, Technical Program Managers, and Design Technologists, working collaboratively.

• Prototyping Architects are hands-on builders, responsible for architecting and coding AI solutions.

• Technical Program Managers facilitate customer engagements and project timelines.

• Design Technologists likely focus on user experience and the integration of AI into user-facing applications.

• The team operates with a high degree of autonomy, driving innovation at the "bleeding edge" of technology. Methodology:

• Data-Driven Innovation: Decisions are informed by customer needs and the potential for AI to drive measurable business outcomes.

• Rapid Prototyping: Emphasis on building functional prototypes in days and weeks, not months, to demonstrate value quickly.

• Experimentation: High-judgment experimentation is encouraged as a catalyst for breakthrough innovation.

• Customer-Centricity: Deep understanding of customer challenges to craft tailored AI solutions.

• Agile Development: Adherence to iterative, agile methodologies to adapt quickly to evolving requirements and technologies.

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

📝 Enhancement Note: The company culture within AWS, and specifically the PACE team, is characterized by innovation, speed, and customer obsession. The team operates with a startup-like agility within a large enterprise, focusing on cutting-edge AI technologies and their practical application for business impact.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a highly technical and customer-facing architect specializing in advanced AI technologies. It sits within the "Prototyping Architect" designation, which implies a senior individual contributor role focused on hands-on creation and technical advisory. It's a critical component of the GTM strategy, enabling sales through technical demonstrations and solution building.

Reporting Structure: Prototyping Architects typically report to a Technical Program Manager or a manager overseeing the PACE team. They work closely with sales teams, solution architects, and AWS service teams.

Operations Impact: The impact is significant, as the PACE team directly influences customer adoption of AWS AI services. By building successful prototypes and demonstrating tangible business value, this role accelerates sales cycles, deepens customer relationships, and drives revenue growth through the adoption of cutting-edge AI solutions. The team's work shapes how enterprises globally approach AI adoption, positioning AWS as a leader in this transformative technology.

Growth Opportunities:

• Specialization: Deepen expertise in specific AI domains like Generative AI, Agentic AI, multi-agent systems, or specific AWS AI services (e.g., advanced SageMaker capabilities).

• Leadership: Transition into Technical Program Management roles, team leadership, or broader Solution Architecture leadership within AWS.

• Thought Leadership: Develop a public profile through speaking engagements, publishing technical content, and contributing to open-source AI frameworks.

• Cross-Functional Mobility: Move into product development roles within AWS AI services, or roles focused on AI strategy and consulting.

• Global Exposure: Opportunity to work with diverse customers across various industries globally, gaining broad market insights.

📝 Enhancement Note: This role offers a unique path for technically skilled individuals to directly impact customer success and revenue through the application of cutting-edge AI. The growth trajectory emphasizes both deepening technical expertise and developing leadership and strategic communication skills, essential for advancing in the AI and cloud industry.

🌐 Work Environment

Office Type: On-site role in Shenzhen, China, within an AWS office environment. This suggests a modern, professional workspace designed to foster collaboration and innovation.

Office Location(s): Shenzhen, Guangdong Province, China. This location places the role within a major technology hub in China, offering access to a vibrant tech ecosystem.

Workspace Context:

• Collaborative Environment: The team structure emphasizes close collaboration with Technical Program Managers, Design Technologists, and potentially sales teams. Expect regular team meetings, stand-ups, and brainstorming sessions.

• Tools and Technology: Access to state-of-the-art AWS AI services, development tools (e.g., Cursor, Kiro, Q Developer), and cloud infrastructure. High-performance computing resources are likely available for AI model development and deployment.

• Team Interaction: Frequent interaction with colleagues on the PACE team and potentially with customer technical teams. Opportunities to engage with AWS service teams and leadership.

• Customer Engagement: Significant portion of time may be spent working directly with customers, either on-site at their locations or remotely, requiring adaptability and strong presentation skills.

Work Schedule: While a standard 40-hour work week is typical, the nature of customer-facing roles and rapid prototyping often necessitates flexibility. Projects may require extended hours to meet critical deadlines or to engage with customers in different time zones. Travel (estimated at 25%) is also a component of the work schedule.

📝 Enhancement Note: The work environment is dynamic and fast-paced, typical of innovation-focused teams within large tech companies. The emphasis on collaboration and direct customer engagement means candidates should be comfortable working in a team setting and interacting with external stakeholders regularly.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or recruiter will review applications for basic qualifications and alignment with the role.

• Technical Assessment: Expect coding challenges and technical deep-dives focusing on AI/ML concepts, system design, programming languages (Python, Java), and cloud architecture. This may include live coding or take-home assignments.

• Architectural Design: Questions will assess your ability to design scalable, reliable, and production-ready AI systems, including prompt engineering, RAG, and agentic architectures.

• Customer Engagement Simulation: You may be asked to walk through how you would approach a customer problem, explain complex AI concepts to a non-technical audience, or present a prototype.

• Behavioral Interviews: Questions will evaluate your experience with agile methodologies, teamwork, problem-solving, and your alignment with AWS's leadership principles.

• Hiring Manager Interview: A final discussion to assess overall fit, strategic thinking, and alignment with the team's goals.

Portfolio Review Tips:

• Highlight AI/ML Impact: Focus on prototypes that demonstrate significant business value or solve complex customer challenges using Generative AI and Agentic AI.

• Showcase Technical Depth: Clearly articulate the architectural decisions, algorithms, and technologies used, especially AWS services like Bedrock and SageMaker.

• Demonstrate Rapid Prototyping: Include examples that show your ability to deliver working solutions quickly, highlighting the tools and methodologies used to accelerate development.

• Explain Customer Collaboration: Detail how you partnered with customers, understood their needs, and translated them into technical solutions.

• Quantify Results: Whenever possible, use metrics to demonstrate the impact of your prototypes (e.g., performance improvements, cost savings, enhanced user experience).

• Code Samples: Be prepared to share well-documented code samples or links to public repositories (e.g., GitHub) showcasing your proficiency.

Challenge Preparation:

• System Design: Practice designing scalable, fault-tolerant systems, with a specific focus on AI/ML pipelines, LLM deployment, and multi-agent orchestration.

• Coding Proficiency: Sharpen your skills in Python and Java, focusing on libraries and frameworks relevant to AI/ML and cloud development.

• AI/ML Concepts: Review core concepts of Generative AI, LLMs, prompt engineering, RAG, agentic workflows, and common AI model deployment patterns.

• AWS Services: Familiarize yourself with key AWS AI/ML services (Bedrock, SageMaker) and relevant cloud-native services (Lambda, Kubernetes).

• Communication Skills: Practice explaining technical concepts clearly and concisely to both technical and non-technical audiences.

📝 Enhancement Note: The interview process is rigorous and designed to assess both deep technical expertise in AI and practical application skills. A strong portfolio that clearly demonstrates hands-on experience with Generative AI, Agentic AI, and AWS services is critical for success.

🛠 Tools & Technology Stack

Primary Tools:

• AWS AI Services: AWS Bedrock, AWS SageMaker, Kiro/Q Developer (AI-driven development tools).

• AI Development Tools: Cursor, Cline, Windsurf (AI-powered IDEs/CLIs and development accelerators).

• Programming Languages: Python (primary), Java, R, Scala.

• Frameworks & Libraries: Spark, Kafka, Hadoop ecosystem, Presto, Hive, Teradata, Elasticsearch.

Analytics & Reporting:

• Tools for monitoring AI model performance, prototype usage, and customer engagement metrics.

• Potentially AWS analytics services like Amazon QuickSight or CloudWatch for dashboarding and reporting. CRM & Automation:

• While not explicitly mentioned, experience with CRM systems (e.g., Salesforce) for understanding customer sales cycles and engagement is beneficial.

• Understanding of workflow automation tools and techniques relevant to AI-driven processes.

• Familiarity with CI/CD pipelines and DevOps practices for rapid deployment of prototypes.

📝 Enhancement Note: Proficiency with AWS's AI/ML suite, particularly Bedrock and SageMaker, is paramount. Experience with AI-driven development tools is a key differentiator. Candidates should also demonstrate a broad understanding of programming languages and data processing frameworks relevant to building and deploying AI solutions.

👥 Team Culture & Values

Operations Values:

• Customer Obsession: Deeply understanding and working backward from customer needs to deliver impactful AI solutions.

• Bias for Action: Emphasizing rapid prototyping and experimentation to drive innovation and deliver results quickly.

• Invent and Simplify: Developing novel AI solutions and simplifying complex technical challenges for customers.

• Ownership: Taking responsibility for building and delivering successful AI prototypes and customer engagements.

• High Standards: Striving for excellence in technical execution, customer communication, and solution quality.

• Data-Driven Approach: Using data and metrics to inform decisions, measure impact, and drive continuous improvement in AI solutions.

Collaboration Style:

• Cross-functional Integration: Working closely with Technical Program Managers, Design Technologists, sales teams, and AWS service teams to deliver comprehensive customer solutions.

• Open Communication: Encouraging open dialogue, knowledge sharing, and constructive feedback within the team to foster innovation.

• Partnership: Building strong relationships with customers, acting as a trusted technical advisor to guide their AI adoption journey.

• Agile and Iterative: Embracing a collaborative, iterative approach to development, adapting quickly to feedback and evolving requirements.

📝 Enhancement Note: The PACE team culture is built around rapid innovation, customer focus, and technical excellence. Collaboration is key, with a strong emphasis on cross-functional teamwork and direct engagement with customers to drive adoption of advanced AI technologies.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapidly Evolving AI Landscape: Keeping pace with the constant advancements in Generative AI and Agentic AI technologies requires continuous learning and adaptation.

• Customer Complexity: Addressing diverse customer needs across various industries and technical maturity levels, translating complex AI into tangible business value.

• Balancing Speed and Production Readiness: Architecting and building prototypes quickly while ensuring they are robust and demonstrate production-ready capabilities.

• Technical Trade-offs: Making informed decisions about LLM selection, agent design, and architectural patterns that balance performance, cost, and scalability.

• Influencing Adoption: Effectively demonstrating the value of AI to diverse stakeholders, including technical teams and executive leadership, to drive adoption.

Learning & Development Opportunities:

• Deep Dive into AI/ML: Opportunities to work with state-of-the-art Generative AI and Agentic AI models and techniques.

• AWS Certification & Training: Access to extensive AWS training resources and certifications to deepen cloud and AI expertise.

• Industry Conferences & Events: Participation in leading AI and cloud conferences to stay abreast of industry trends and network with peers.

• Mentorship Programs: Guidance from experienced architects and leaders within AWS.

• Exposure to Diverse Industries: Working with customers across various sectors provides broad business and technical insights.

📝 Enhancement Note: This role presents significant challenges due to the cutting-edge nature of the technology and the fast-paced customer engagement model. However, these challenges are also drivers for substantial professional growth, offering unparalleled opportunities to become an expert in AI and cloud solutions.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you architected and built a complex AI prototype. What were the key technical challenges, and how did you overcome them?" (Focus on architecture, problem-solving, and AI/ML specifics).

• "How would you explain the concept of an autonomous agent and its potential business applications to a non-technical executive?" (Assess communication skills and ability to translate technical concepts).

• "Walk me through your process for selecting an LLM for a specific customer use case. What factors do you consider, and what are the trade-offs?" (Evaluate understanding of LLM selection criteria and strategic thinking).

• "Imagine a customer wants to build a multi-agent system for customer service automation. What would be your initial architectural approach, and what AWS services would you leverage?" (Test system design and AWS service knowledge). Company & Culture Questions:

• "What interests you about working at AWS and specifically on the PACE team?" (Assess motivation and alignment with AWS/PACE mission).

• "How do you stay current with the rapidly evolving field of Generative AI?" (Gauge commitment to continuous learning).

• "Describe a situation where you had to work with a difficult stakeholder or overcome resistance to a new technology." (Evaluate collaboration and influence skills).

• "How do you embody AWS's leadership principles like 'Customer Obsession' or 'Bias for Action' in your work?" (Assess cultural fit). Portfolio Presentation Strategy:

• Storytelling: Frame your portfolio pieces as compelling stories that highlight the customer's problem, your solution, the technical approach, and the resulting impact.

• Visual Aids: Use clear diagrams, screenshots, and concise code snippets to illustrate your points effectively.

• Metrics-Driven: Quantify the success of your prototypes with relevant metrics wherever possible (e.g., performance gains, efficiency improvements, user adoption rates).

• Technical Depth & Breadth: Be prepared to dive deep into the technical details of your projects while also explaining the broader business context and strategic implications.

• Interactive Demos: If possible, prepare short, interactive demos of your prototypes to showcase functionality live.

📝 Enhancement Note: Interview preparation should focus on demonstrating a strong technical foundation in AI/ML, practical experience with AWS services, and the ability to communicate complex technical solutions effectively to diverse audiences. The portfolio is a critical component, so practice presenting it clearly and concisely.

📌 Application Steps

To apply for this operations position:

• Submit your application through the Amazon Jobs portal via the provided URL.

• Curate Your Portfolio: Carefully select 2-3 of your most impactful projects that showcase your expertise in Generative AI, Agentic AI, AWS services (Bedrock, SageMaker), and rapid prototyping. Ensure each project clearly outlines the problem, your solution, technical implementation, and measurable results.

• Optimize Your Resume: Tailor your resume to highlight keywords and skills mentioned in the job description, such as "Generative AI," "Agentic AI," "Python," "AWS," "System Design," "Prompt Engineering," and "Cloud Computing." Quantify your achievements with specific metrics whenever possible.

• Prepare Your Presentation: Practice a concise and compelling presentation of your portfolio. Be ready to articulate your technical decisions, explain complex concepts, and demonstrate how your work drives business value for customers.

• Research AWS & PACE: Understand AWS's mission, its AI strategy, and the specific goals of the PACE team. Familiarize yourself with their customer-centric approach and commitment to 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.