Sr. Prototyping Architect, PACE, AWS Prototyping and AI Customer Engineering (PACE)

📍 Job Overview

Job Title: Sr. Prototyping Architect, PACE, AWS Prototyping and AI Customer Engineering (PACE)

Company: Amazon

Location: Mexico City, Mexico

Job Type: Full-Time

Category: Engineering / AI / Cloud Solutions

Date Posted: April 20, 2026

Experience Level: 10+ Years

Remote Status: On-site

🚀 Role Summary

• Architect and develop functional Generative AI and Agentic AI prototypes directly with customers using AWS AI services, demonstrating a clear path to production solutions.

• Leverage AI-driven development tools and modern engineering practices to rapidly build and iterate on prototypes, implementing advanced AI patterns.

• Guide customers through complex technical decisions on AI adoption strategies, translating business requirements into actionable technical approaches for their operations.

• Partner with cross-functional teams, including Technical Program Managers and field teams, to deliver customer engagements and influence AWS AI product roadmaps.

• Develop and share reusable agent frameworks, code libraries, reference architectures, and thought leadership content to accelerate Generative AI and Agentic AI adoption.

📝 Enhancement Note: This role is deeply embedded in customer-facing technical challenges, requiring a blend of advanced AI/ML expertise, robust software engineering principles, and strong communication skills. The focus on "prototyping" implies rapid development cycles and a hands-on approach to demonstrating the "art of the possible" with AWS AI services, directly impacting customer GTM strategies and operational efficiency.

📈 Primary Responsibilities

• Architect and develop functional Generative AI and Agentic AI prototypes directly with customers using AWS AI services like Amazon Bedrock and Amazon SageMaker, including autonomous agents, multi-agent systems, RAG architectures, and LLM-powered applications that demonstrate a clear path to production.

• Leverage AI-driven development tools and modern engineering practices to rapidly build and iterate on prototypes, implementing patterns such as prompt engineering, function calling, agent orchestration, and tool use to optimize operational workflows.

• Guide customers through complex technical decisions on LLM selection, agent design patterns, agentic architectures, and AI adoption strategies, translating business requirements into actionable technical approaches for their specific business operations.

• Partner with Technical Program Managers, Design Technologists, and field teams to deliver customer engagements on time and with high quality, while providing critical feedback to AWS service teams to influence AI product roadmaps and future service development.

• Develop and share agent frameworks, code libraries, reference architectures, whitepapers, blogs, and conference presentations that accelerate Generative AI and Agentic AI adoption across the AWS customer base, establishing best practices for operational implementation.

• Continuously research and experiment with new AI/ML capabilities, agentic patterns, and cloud-native architectures to bring innovative solutions to customer engagements, ensuring clients stay ahead of market trends.

• Travel up to 25% within LATAM to engage directly with customers and understand their unique operational challenges and opportunities.

📝 Enhancement Note: The responsibilities emphasize a hands-on, customer-centric approach to AI solution development, focusing on practical application and production readiness. The role requires not only technical depth but also the ability to translate complex AI concepts into tangible business value for customers, impacting their operational efficiency and market competitiveness.

🎓 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 a role of this seniority and technical depth.

Experience: 8+ years of experience in specific technology domain areas such as software development, cloud computing, systems engineering, infrastructure, security, networking, or data & analytics. A proven track record of architecting and delivering production-grade solutions on AWS is highly valued.

Required Skills:

• 8+ years of specific technology domain areas (e.g., software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience.

• Working knowledge of AI/ML technologies, with a particular interest in or exposure to Generative AI, large language models (LLMs), or emerging AI technologies.

• Strong software development skills with a proficiency in modern programming languages (e.g., Python, Java, Go).

• Deep understanding of cloud computing principles and architectures, particularly within the AWS ecosystem.

• Experience with system design, architecture patterns, and best practices for scalable and resilient systems.

Preferred Skills:

• Proven experience architecting production-grade solutions on AWS, with specific experience in generative AI and large language model services.

• Hands-on experience building agentic AI systems: multi-agent orchestration, tool use, autonomous reasoning patterns.

• Proficiency in leveraging AI-driven development workflows to accelerate prototyping and delivery, including iterative development and testing.

• Track record of delivering customer prototypes and Proofs of Concept (POCs) that shaped enterprise adoption strategies and demonstrated clear business value.

• Familiarity with generative AI concepts such as RAG (Retrieval Augmented Generation) architectures, prompt engineering techniques, and LLM fine-tuning.

• Experience with autonomous agents, multi-agent systems, and agent orchestration frameworks.

• AWS Certifications (e.g., Solutions Architect, Developer, Machine Learning Specialty) or equivalent practical experience.

• Experience working in customer-facing roles or technical consulting environments.

• Familiarity with agile development methodologies and rapid prototyping cycles.

📝 Enhancement Note: The "8+ years" requirement, coupled with the "10+ years" AI-derived experience level, suggests a strong preference for senior candidates who can lead complex technical initiatives. The emphasis on preferred qualifications like "architecting production-grade solutions on AWS" and "hands-on experience building agentic AI systems" indicates a need for practical, in-depth expertise rather than just theoretical knowledge.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstration of AI/ML Project Execution: Showcase projects where you've architected and developed functional prototypes or production-ready solutions leveraging Generative AI, Agentic AI, or LLMs.

• AWS Cloud Architecture Expertise: Provide examples of complex solutions built on AWS, highlighting architectural decisions, scalability considerations, and integration patterns relevant to operational systems.

• Prototyping and POC Experience: Include case studies of rapid prototyping or Proof of Concept (POC) engagements, detailing the problem statement, your approach, the tools used, and the impact on customer adoption strategy.

• Code and Framework Contributions: If applicable, share examples of reusable code libraries, agent frameworks, or reference architectures you've developed that accelerate AI development and deployment.

Process Documentation:

• Workflow Design and Optimization: Demonstrate experience in designing and optimizing complex workflows, particularly those involving AI agents, data processing pipelines, or customer interaction processes.

• Implementation and Automation Methods: Provide examples of how you've implemented AI solutions and automated operational tasks using cloud services and modern development practices.

• Measurement and Performance Analysis: Showcase your ability to define key performance indicators (KPIs) for AI prototypes and operational systems, and how you've measured and analyzed performance to drive improvements.

📝 Enhancement Note: For this role, a portfolio demonstrating hands-on experience with AI/ML prototyping on AWS is crucial. Candidates should be prepared to showcase not just completed projects but also their thought process, architectural decisions, and the iterative nature of prototyping, especially concerning customer-facing applications and operational efficiency gains.

💵 Compensation & Benefits

Salary Range: Given the Sr. Prototyping Architect title, 10+ years of experience, specialized AI/ML and AWS expertise, and the location in Mexico City, Mexico, a competitive salary range would typically fall between MXN 1,500,000 - MXN 2,500,000 annually. This estimate is based on industry benchmarks for senior technical roles in major LATAM tech hubs, considering the high demand for AI and cloud expertise.

Benefits:

• Comprehensive health, dental, and vision insurance plans.

• Generous paid time off (PTO) and holidays.

• Retirement savings plans with company matching.

• Employee stock purchase program (ESPP).

• Professional development and continuous learning opportunities, including AWS certifications and training.

• Access to cutting-edge AI/ML technologies and tools.

• Opportunities for international travel and collaboration within LATAM.

• Employee assistance programs for well-being and support.

• Relocation assistance, if applicable.

Working Hours: A standard full-time work week is typically 40 hours, with flexibility expected due to the nature of customer engagements and project deadlines. The role may require occasional work outside of standard business hours to support customer needs or critical project milestones.

📝 Enhancement Note: The salary estimate is based on current market data for senior engineering roles with AI and cloud specialization in Mexico City. Amazon is known for offering competitive compensation packages that include base salary, stock options (RSUs), and comprehensive benefits.

🎯 Team & Company Context

🏢 Company Culture

Industry: Cloud Computing / Artificial Intelligence / E-commerce / Technology Services. Amazon Web Services (AWS) operates at the forefront of cloud technology, providing a vast array of services that power businesses globally. The company has a significant presence in AI and Machine Learning, driving innovation across various sectors.

Company Size: Amazon is a massive global corporation with hundreds of thousands of employees worldwide, categorized as a "Mega-company" (10,000+ employees). This size offers extensive resources, global reach, and diverse career opportunities.

Founded: Amazon was founded in 1994 by Jeff Bezos. AWS was launched in 2006, rapidly becoming a leading cloud provider. This history signifies a culture of continuous innovation, customer obsession, and a long-term vision for technological advancement.

Team Structure:

• PACE Team: The Prototyping and AI Customer Engineering (PACE) team is described as an "elite group of hands-on builders." This suggests a highly skilled, agile, and collaborative unit focused on rapid innovation.

• Reporting: The role involves partnering with Technical Program Managers (TPMs) and field teams, indicating a matrixed reporting structure where collaboration and influence are key.

• Cross-functional Collaboration: The role requires close collaboration with TPMs, Design Technologists, field teams, and AWS service teams, emphasizing a cross-functional and team-oriented approach to problem-solving.

Methodology:

• Agile Prototyping: The team operates using rapid, time-boxed prototyping engagements, emphasizing speed, experimentation, and iteration.

• Customer-Centric Approach: A core methodology involves understanding customer needs deeply and partnering with them to experiment with new ideas and solutions.

• Data-Driven Experimentation: The culture encourages moving fast, learning from failures, and iterating toward breakthrough outcomes, implying a strong reliance on feedback loops and data from experiments.

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

📝 Enhancement Note: Amazon's culture is characterized by its Leadership Principles, which emphasize customer obsession, ownership, bias for action, and continuous learning. The PACE team specifically embodies this by operating at the frontier of AI technology and focusing on delivering tangible value through rapid prototyping.

📈 Career & Growth Analysis

Operations Career Level: This role is at a Senior Architect level, focusing on specialized technical domains (AI/ML, Generative AI, Agentic AI) within a customer engineering context. It's a hands-on, individual contributor role with significant technical leadership and customer-facing responsibilities, requiring deep expertise and the ability to guide others.

Reporting Structure: The Sr. Prototyping Architect will likely report to a manager within the PACE organization, working closely with Technical Program Managers (TPMs) and collaborating extensively with field teams and AWS service teams. This structure emphasizes influence and collaboration over direct authority.

Operations Impact: The impact of this role is significant, focusing on demonstrating the "art of the possible" with AWS AI services for customers. Success directly translates to:

• Accelerated Customer Adoption: By showcasing practical, production-ready prototypes, the role helps customers overcome adoption hurdles and speed up their AI/ML initiatives.

• New Market Exploration: Prototypes can reveal new use cases and business models, opening up new markets or transforming existing service delivery.

• Product Roadmap Influence: Feedback from customer engagements directly influences the development and roadmap of AWS AI services, shaping the future of cloud AI offerings.

• Operational Efficiency: Demonstrating AI solutions can lead to significant improvements in customer operational efficiency, cost reduction, and enhanced service delivery.

Growth Opportunities:

• Deep Specialization: Opportunity to become a leading expert in Generative AI, Agentic AI, and AWS AI services, staying at the cutting edge of a rapidly evolving field.

• Technical Leadership: Grow into more senior architect roles, leading larger engagements, mentoring junior architects, and contributing to architectural best practices across AWS.

• Cross-Functional Exposure: Gain deep experience working with various AWS service teams, field sales, and customer executives, broadening understanding of the cloud ecosystem and business challenges.

• Thought Leadership: Develop and share knowledge through whitepapers, blogs, and conference presentations, building a personal brand and contributing to the broader AI community.

• Potential for Management: While primarily an individual contributor role, strong performance and leadership can open doors to management positions within PACE or other AWS organizations.

📝 Enhancement Note: The role offers a unique blend of deep technical expertise, customer engagement, and influence on product development, providing a rich environment for career growth within the rapidly expanding field of AI on AWS.

🌐 Work Environment

Office Type: The role is on-site in Mexico City, Mexico, within an Amazon office environment. Amazon offices are typically modern, well-equipped workspaces designed to foster collaboration and productivity.

Office Location(s): Mexico City, Mexico. Specific office address details would be provided during the application process.

Workspace Context:

• Collaborative Spaces: Expect access to meeting rooms, collaboration zones, and potentially dedicated project spaces for customer engagements and team sync-ups.

• Technology Access: The workspace will be equipped with necessary computing resources, high-speed internet, and access to AWS development tools and platforms.

• Team Interaction: Regular face-to-face interaction with fellow Prototyping Architects, Technical Program Managers, and other team members is expected, facilitating knowledge sharing and problem-solving.

Work Schedule: The standard work schedule is typically 40 hours per week. However, the nature of customer engagements and project deadlines may require flexibility, with occasional work outside of standard business hours to ensure timely delivery and customer satisfaction. The "A day in the life" section suggests that while coding is a significant part, there's also time for team meetings, customer calls, and documentation.

📝 Enhancement Note: The on-site requirement in Mexico City suggests a preference for candidates who can actively participate in team dynamics and customer interactions within a physical office setting. This environment is designed to support rapid prototyping and collaborative problem-solving.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter will likely conduct an initial screening call to assess basic qualifications, interest, and cultural fit.

• Technical Phone Screen(s): Expect one or more technical interviews focusing on core concepts in software engineering, cloud computing (AWS), and AI/ML.

These may include coding challenges or system design questions.

• On-site/Virtual Loop: A comprehensive interview loop, typically involving 4-6 interviews with different team members, including:

  • Technical Deep Dives: In-depth discussions on specific AI/ML technologies, generative AI patterns, agentic systems, and AWS services.
  • System Design: Architecting complex solutions, potentially involving AI components, with a focus on scalability, reliability, and production readiness.
  • Behavioral Interviews: Questions based on Amazon's Leadership Principles (e.g., Customer Obsession, Bias for Action, Ownership, Dive Deep).
  • Portfolio Presentation: A dedicated session to walk through selected portfolio projects, explaining your role, technical approach, challenges, and outcomes.

• Hiring Manager Interview: A final interview with the hiring manager to discuss overall fit, career aspirations, and strategic alignment.

Portfolio Review Tips:

• Curate Strategically: Select 2-3 of your most impactful projects, focusing on those that best demonstrate your experience with Generative AI,

Agentic AI, AWS, and customer-facing prototyping.

• Structure Your Case Studies: For each project, clearly outline:

  • The Problem: What was the customer's business challenge or opportunity?
  • Your Role & Contribution: What specific tasks did you perform?
  • The Solution: Describe the architecture, technologies used (especially AWS AI services), and key design decisions.
  • The Process: Detail your prototyping methodology, iterative development, and how you incorporated feedback.
  • The Outcome/Impact: Quantify results where possible (e.g., efficiency gains, new capabilities demonstrated, path to production).

• Highlight AWS Expertise: Explicitly mention the AWS services used and how they contributed to the solution's success.

• Showcase AI/ML Depth: Explain your approach to prompt engineering, agent orchestration, RAG, or other relevant AI concepts.

• Prepare for Technical Questions: Be ready to answer detailed questions about your code, architectural choices, and trade-offs made.

Challenge Preparation:

• Coding Proficiency: Practice coding challenges, particularly in Python, focusing on algorithm efficiency and data structure manipulation.

• System Design Scenarios: Prepare for system design questions that may involve building scalable, fault-tolerant systems, potentially incorporating AI components.

• AWS Knowledge: Refresh your understanding of core AWS services (EC2, S3, Lambda, SageMaker, Bedrock) and AI/ML services.

• Amazon Leadership Principles: Familiarize yourself with Amazon's 16 Leadership Principles and prepare specific examples from your experience that demonstrate each.

📝 Enhancement Note: The emphasis on a portfolio and customer engagements means interviewers will be looking for not just technical skill but also the ability to communicate complex technical concepts clearly, demonstrate practical problem-solving, and articulate business impact.

🛠 Tools & Technology Stack

Primary Tools:

• AWS AI Services: Amazon Bedrock, Amazon SageMaker, and other relevant AWS AI/ML services are central to this role.

• Generative AI Frameworks: Tools for prompt engineering, model fine-tuning, and LLM integration.

• Agentic AI Frameworks: Libraries and platforms for building multi-agent systems, orchestration, and tool use (e.g., LangChain, AutoGen, or proprietary AWS frameworks).

• Programming Languages: Python is highly likely to be a primary language for AI/ML development and scripting. Other languages like Java, Go, or Node.js may also be relevant for backend services or integrations.

Analytics & Reporting:

• AWS Analytics Services: Services like Amazon QuickSight, AWS Glue, and Amazon Athena may be used for data preparation and analysis of prototype performance.

• Monitoring Tools: AWS CloudWatch for monitoring application performance, logging, and debugging.

CRM & Automation:

• AWS Services for Automation: Lambda functions for serverless automation, Step Functions for orchestrating workflows, and potentially services for CI/CD pipelines.

• Version Control: Git (e.g., GitHub, GitLab, AWS CodeCommit) for code management and collaboration.

📝 Enhancement Note: Proficiency with the AWS ecosystem, especially its AI and ML services, is paramount. Candidates should be prepared to demonstrate hands-on experience with these tools and frameworks.

👥 Team Culture & Values

Operations Values:

• Customer Obsession: Deeply understanding and working backward from customer needs to deliver innovative AI solutions that solve real operational challenges.

• Bias for Action: Moving quickly to build and test prototypes, iterating based on feedback and data to achieve desired outcomes.

• Ownership: Taking responsibility for the success of customer engagements, from initial concept to demonstrating a clear path to production.

• Invent and Simplify: Continuously seeking new ways to solve problems, simplifying complex AI concepts and architectures for customers.

• Dive Deep: Thoroughly understanding customer business operations, technical requirements, and the intricacies of AI/ML technologies.

• Insist on the Highest Standards: Ensuring prototypes are well-architected, robust, and provide a solid foundation for future production deployments.

Collaboration Style:

• Cross-functional Partnership: Working closely with TPMs, Design Technologists, and field teams to ensure holistic solution delivery.

• Knowledge Sharing: Actively contributing to the team's collective knowledge through documentation, code sharing, and presentations.

• Feedback Integration: Open to receiving and providing constructive feedback to improve prototypes, processes, and team performance.

• Customer Engagement: Collaborative approach with customers, acting as a trusted advisor and technical partner.

📝 Enhancement Note: Amazon's culture is deeply ingrained in its Leadership Principles. For this role, principles like Customer Obsession, Bias for Action, and Invent and Simplify will be particularly relevant in daily work and team interactions.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapidly Evolving AI Landscape: Staying current with the fast-paced advancements in Generative AI, Agentic AI, and LLMs requires continuous learning and adaptation.

• Translating Vision to Reality: Effectively demonstrating the "art of the possible" and ensuring prototypes provide a viable path to production for diverse customer operations.

• Balancing Speed and Quality: Delivering rapid prototypes while adhering to production-grade engineering best practices.

• Complex Customer Requirements: Addressing unique and often ambiguous operational challenges presented by a wide range of customers.

• Cross-functional Coordination: Navigating the complexities of working with multiple internal teams and external stakeholders to ensure successful engagement delivery.

Learning & Development Opportunities:

• Cutting-edge AI Exposure: Direct involvement with the latest AWS AI services and emerging AI technologies.

• Specialized Training: Access to AWS training, certifications, and resources to deepen expertise in AI/ML and cloud architecture.

• Mentorship: Opportunities to learn from experienced architects and leaders within the PACE team and broader AWS organization.

• Industry Engagement: Potential to present at conferences, write blogs, and contribute to the AI/ML community, enhancing professional visibility.

• Career Pathing: Clear pathways for advancement into senior architect, principal architect, or management roles within AWS.

📝 Enhancement Note: This role is ideal for individuals who thrive on technical challenges, enjoy continuous learning, and are motivated by the opportunity to shape the future of AI adoption for businesses.

💡 Interview Preparation

Strategy Questions:

• "Tell me about a time you built a complex prototype or POC for a customer. What was the challenge, your approach, and the outcome?" (Focus on demonstrating your process, technical choices, and impact on customer operations.)

• "How would you design an agentic system to automate [specific customer operational task, e.g., customer support ticket routing, inventory management] using AWS services like Bedrock and SageMaker?" (Prepare to discuss architecture, LLM selection, agent orchestration, data handling, and scalability.)

• "Describe a situation where you had to balance rapid development speed with the need for production-ready code. What trade-offs did you make?" (Highlight your understanding of engineering best practices and how you applied them in a fast-paced prototyping environment.)

Company & Culture Questions:

• "Why are you interested in working for Amazon and specifically the PACE team?" (Connect your passion for AI and prototyping with Amazon's customer-obsessed culture and the PACE team's mission.)

• "How do you embody Amazon's Leadership Principles, such as 'Customer Obsession' or 'Bias for Action'?" (Prepare specific, STAR method-based examples.)

Portfolio Presentation Strategy:

• Focus on Impact: For each project, clearly articulate the business problem solved and the value delivered to the customer's operations.

• Explain Your 'Why': Be prepared to justify your architectural decisions and technology choices.

• Showcase Iteration: Highlight how you incorporated feedback and iterated on your prototypes.

• Technical Depth: Be ready to dive deep into the technical details of your solutions when asked.

• Conciseness: Present your projects efficiently, ensuring you cover the key aspects without getting bogged down in unnecessary detail.

📝 Enhancement Note: Preparing concrete examples that align with Amazon's Leadership Principles and demonstrate your technical expertise in AI, AWS, and prototyping will be critical for success.

📌 Application Steps

To apply for this operations position:

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

• Resume Optimization: Tailor your resume to highlight your experience with Generative AI, Agentic AI, AWS services (particularly Bedrock and SageMaker), software architecture, prototyping, and customer-facing roles. Use keywords from the job description to ensure ATS compatibility.

• Portfolio Curation & Practice: Select your strongest projects that showcase your AI/ML prototyping skills on AWS. Practice presenting these projects, focusing on business impact, technical approach, and lessons learned.

• Technical & Behavioral Preparation: Review core computer science fundamentals, AWS services, AI/ML concepts, and prepare specific examples using the STAR method for Amazon's Leadership Principles.

• Company Research: Familiarize yourself with Amazon's culture, the PACE team's mission, and recent AWS AI announcements to demonstrate your understanding and enthusiasm.

⚠️ 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.

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