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

📍 Job Overview

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

Company: Amazon

Location: Mexico City, Mexico

Job Type: Full-time

Category: Cloud Engineering / AI / Software Development

Date Posted: 2026-06-08

Experience Level: 10+ Years

Remote Status: On-site

🚀 Role Summary

• Architect and develop cutting-edge Generative AI and Agentic AI prototypes for AWS customers, demonstrating a clear path to production.

• Collaborate closely with customers to experiment with innovative ideas, leveraging AWS AI services to open new markets and transform service delivery.

• Utilize modern engineering practices and AI-driven development tools to rapidly build and iterate on functional prototypes, focusing on areas like autonomous agents, multi-agent systems, and RAG architectures.

• Contribute to influencing AWS AI product roadmaps by providing direct customer feedback and developing reusable frameworks, code libraries, and reference architectures.

📝 Enhancement Note: This role is highly specialized within AWS's customer-facing engineering organization, focusing on the practical application and rapid prototyping of Generative AI and Agentic AI technologies. It requires a blend of deep technical expertise in AI/ML and cloud infrastructure, coupled with strong customer engagement and solutioning skills. The "PACE" designation suggests a focus on speed, agility, and customer-centric development. This is not a traditional sales or pure consulting role but a hands-on technical architect position focused on demonstrating the "art of the possible" with AI on AWS.

📈 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.

• 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.

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

• 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.

• Continuously research and experiment with new AI/ML capabilities, agentic patterns, and cloud-native architectures to bring innovative solutions to customer engagements.

• Position requires up to 25% travel within LATAM.

📝 Enhancement Note: The responsibilities highlight a hands-on, customer-facing role that requires not only deep technical expertise in Generative AI and Agentic AI but also the ability to translate complex technical concepts into tangible prototypes and actionable strategies for customers. The emphasis on "demonstrating a clear path to production" indicates a need for practical, production-ready solutions, not just proof-of-concepts. The requirement to share knowledge through various channels (whitepapers, blogs, presentations) underscores the importance of thought leadership and community building within the AWS ecosystem.

🎓 Skills & Qualifications

Education: While no specific degree is listed, a Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field is typically expected for roles requiring 10+ years of IT experience.

Experience:

• 10+ years of IT development or implementation/consulting experience in the software or Internet industries.

• 4+ years in specific technology domain areas such as software development, cloud computing, systems engineering, infrastructure, security, networking, or data & analytics.

• 2+ years of design, implementation, or consulting in applications and infrastructures experience. Required Skills:

• Deep expertise in software development and modern engineering practices.

• Proficiency in architecting and developing Generative AI and Agentic AI solutions.

• Hands-on experience with AWS AI services, specifically Amazon Bedrock and Amazon SageMaker.

• Understanding and practical application of patterns like RAG architectures, prompt engineering, function calling, agent orchestration, and tool use.

• Strong cloud computing knowledge, particularly within the AWS ecosystem.

• Experience in designing and implementing complex applications and infrastructures.

• Ability to translate business requirements into technical approaches and guide customers through technical decisions.

• Excellent communication and presentation skills, with the ability to share technical knowledge effectively. Preferred Skills:

• Experience working within software development or Internet-related industries.

• Experience migrating or transforming legacy customer solutions to the cloud.

• Experience working with AWS technologies from a dev/ops perspective.

• Familiarity with autonomous agents and multi-agent systems.

• Experience in technical consulting or customer-facing roles.

📝 Enhancement Note: The "Basic Qualifications" are substantial, indicating a need for seasoned professionals. The "Preferred Qualifications" suggest that candidates with prior cloud migration and DevOps experience on AWS will have an advantage. The role demands a blend of cutting-edge AI knowledge and robust, traditional software engineering and cloud architecture experience.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrated AI/ML Projects: A portfolio showcasing practical applications of Generative AI, Agentic AI, or Machine Learning, ideally with publicly available code or detailed case studies.

• AWS Service Implementation: Evidence of designing and implementing solutions using core AWS services, with a focus on AI/ML services like Amazon Bedrock and SageMaker.

• Prototype Development: Examples of rapid prototyping, demonstrating the ability to quickly build functional applications that solve specific business problems.

• Architectural Diagrams & Explanations: Clear diagrams and explanations of system architectures, illustrating how different components (LLMs, agents, AWS services) integrate and function.

• Impact & ROI Focus: Case studies that quantify the impact of implemented solutions, highlighting efficiency gains, new market opportunities, or transformation achieved for previous clients or projects.

Process Documentation:

• Workflow Design: Ability to document and explain the design of complex AI workflows, including agent orchestration and RAG pipelines.

• Implementation Best Practices: Documentation or clear articulation of best practices for implementing AI solutions in a production-ready manner on AWS.

• Performance Analysis: Examples of how performance metrics for AI models and prototypes were tracked, analyzed, and improved.

📝 Enhancement Note: For a role focused on prototyping and customer engineering, a portfolio is crucial. It should go beyond theoretical knowledge to showcase tangible creations and the process behind them. Candidates should be prepared to walk through their projects, explaining the technical choices, the challenges faced, and the outcomes achieved. Highlighting experience with AWS services and a clear understanding of how to bridge prototypes to production are key.

💵 Compensation & Benefits

Salary Range: Based on industry benchmarks for a Prototyping Architect with 10+ years of experience in Cloud Engineering and AI, particularly with a major tech company like Amazon and in a high-cost-of-living region like Mexico City, the estimated annual salary range would be approximately MXN $1,500,000 - MXN $2,500,000. This range can vary significantly based on exact experience, specific skillset, negotiation, and performance.

Benefits:

• Comprehensive health, dental, and vision insurance.

• Retirement savings plans (e.g., Afore contributions).

• Stock options or Restricted Stock Units (RSUs) as part of compensation.

• Generous paid time off (PTO), including vacation, sick leave, and holidays.

• Employee discounts on Amazon products and services.

• Professional development opportunities, including training, certifications, and access to internal learning resources.

• Relocation assistance may be provided if applicable.

• Life insurance and disability coverage.

Working Hours: Typically 40 hours per week, with flexibility expected to meet project deadlines and customer engagement needs. Travel to customer sites within LATAM (up to 25%) is also a component of the working hours.

📝 Enhancement Note: Salary estimates for Mexico City for a senior technical role at a global tech giant like Amazon are based on research from reputable salary aggregators (e.g., Glassdoor, LinkedIn Salary, local recruitment agencies) for similar positions in technology and cloud architecture. Benefits are standard for large multinational corporations and may vary slightly by region. The 40-hour work week is a baseline, but roles involving customer engagements and prototyping often require adaptability and extended hours during critical project phases.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology (Cloud Computing, Artificial Intelligence, E-commerce, Digital Streaming, etc.)

Company Size: Large Enterprise (Over 100,000 employees globally)

Founded: 1994

Team Structure:

• The PACE (Prototyping and AI Customer Engineering) team is likely a specialized, agile unit within AWS Customer Engineering. It operates as a highly technical, hands-on group focused on innovation and customer success.

• Team members likely consist of Prototyping Architects, AI/ML Specialists, Software Engineers, and Technical Program Managers.

• The structure is designed for rapid iteration and close collaboration with customers, often working in project-based teams. Methodology:

• Customer-Centric Innovation: Deep focus on understanding customer needs and challenges, then rapidly building solutions to address them.

• Agile Development: Embraces fast-paced development cycles, iteration, and learning from experimentation.

• Data-Driven Experimentation: Utilizes data to inform design choices and measure the success of prototypes, with a willingness to pivot based on results.

• Collaboration and Knowledge Sharing: Strong emphasis on teamwork, cross-functional collaboration, and disseminating learnings through documentation, blogs, and presentations.

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

📝 Enhancement Note: Amazon's culture is famously customer-obsessed and emphasizes innovation, speed, and data-driven decision-making. Within AWS, this translates to a highly technical environment focused on helping customers leverage cloud technologies. The PACE team embodies this by being at the forefront of AI innovation, working directly with clients to showcase the practical applications of cutting-edge technologies.

📈 Career & Growth Analysis

Operations Career Level: This role sits at a senior/lead architect level within the AI and Cloud Engineering domain. It's a hands-on technical leadership position that requires deep expertise and the ability to influence customer technology strategies.

Reporting Structure: The Prototyping Architect likely reports to a manager or principal engineer within the PACE team or a broader AWS Customer Engineering organization. They will work closely with Technical Program Managers, field sales teams, and directly with customer technical stakeholders.

Operations Impact: The impact is significant, as this role directly influences customer adoption of AWS AI services. By building compelling prototypes and demonstrating tangible value, the Prototyping Architect helps customers accelerate their digital transformation, unlock new business opportunities, and deepen their reliance on the AWS platform. This role is crucial in showcasing the "art of the possible" and driving future revenue through successful customer engagements.

Growth Opportunities:

• Technical Specialization: Deepen expertise in specific AI domains (e.g., multimodal AI, advanced agentic systems, specific LLMs).

• Leadership: Transition into Principal Architect roles, team lead positions, or management within AWS Customer Engineering.

• Product Influence: Become a key influencer for AWS AI service roadmaps based on direct customer feedback and prototype insights.

• Thought Leadership: Develop a strong public profile through speaking engagements, publications, and contributing to open-source projects.

• Broader AWS Roles: Opportunities to move into product management, solutions architecture leadership, or strategic roles within AWS.

📝 Enhancement Note: This role offers a clear path for technical growth and leadership within the rapidly expanding field of AI on AWS. The hands-on nature and direct customer interaction provide invaluable experience and visibility, fostering career advancement opportunities within Amazon's vast ecosystem.

🌐 Work Environment

Office Type: This is an on-site role, meaning the Prototyping Architect will work from an Amazon office in Mexico City. The environment will likely be modern, collaborative, and equipped with the necessary technology infrastructure.

Office Location(s): Mexico City, Mexico. Specific office details would be provided upon inquiry or during the interview process.

Workspace Context:

• Collaborative Hubs: Offices will feature open workspaces, meeting rooms, and collaboration zones designed for team interaction and brainstorming.

• Technology-Rich: Access to high-performance computing resources, development tools, and robust network connectivity essential for AI/ML development.

• Cross-Functional Interaction: Opportunities to collaborate daily with fellow architects, engineers, program managers, and potentially customer teams onsite.

Work Schedule: The standard work schedule is likely 40 hours per week, but the nature of customer engagements and prototyping demands flexibility. This may involve extended hours during critical project phases or customer deadlines. Travel up to 25% within LATAM is also a key component of the work schedule.

📝 Enhancement Note: While on-site, the emphasis will be on agile, collaborative work. The office environment in Mexico City will support the fast-paced, innovative nature of the PACE team, providing the tools and spaces needed for rapid prototyping and customer engagement.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Recruiter call to assess basic qualifications, interest, and cultural fit.

• Technical Screening: A call with a hiring manager or senior engineer to delve into technical skills, experience with AI/ML, AWS services, and software engineering fundamentals.

• Deep Dive Interviews (Multiple Rounds):

  • Architectural Design: Problems related to designing scalable, resilient, and efficient AI systems on AWS.
  • Hands-on Coding/Prototyping: Live coding exercises or system design challenges focused on AI/ML algorithms, API integration, or prototype development.
  • Behavioral Questions: Assessing problem-solving, collaboration, customer obsession, and leadership principles (using the STAR method).
  • Portfolio Review: A dedicated session to present and discuss past projects, prototypes, and architectural designs.

• Final Round: Typically with a senior leader to finalize assessment and discuss team fit.

Portfolio Review Tips:

• Curate Select Projects: Choose 2-3 of your most impactful projects that best demonstrate your skills in Generative AI,

Agentic AI, AWS, and prototyping.

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

  • The Problem: What was the customer's challenge or business need?
  • Your Solution: How did you architect and build the prototype/solution? Detail the AWS services and AI technologies used.
  • Your Role: What specifically did you contribute?
  • The Outcome: What were the results? Quantify impact where possible (e.g., efficiency gains, new capabilities).
  • Lessons Learned: What challenges did you overcome, and what did you learn?

• Technical Depth: Be prepared to discuss the technical intricacies, trade-offs made, and alternative approaches considered.

• AWS Focus: Emphasize your proficiency with AWS services, especially Amazon Bedrock, SageMaker, and other relevant AI/ML offerings.

• Presentation Skills: Practice presenting your work clearly, concisely, and engagingly. Be ready to answer detailed technical questions.

Challenge Preparation:

• AWS Architecture: Review AWS Well-Architected Framework principles and best practices for AI/ML workloads.

• AI/ML Concepts: Refresh knowledge on LLMs, RAG, prompt engineering, agent design patterns, and common AI/ML algorithms.

• Coding Proficiency: Practice coding in Python (likely the primary language) and common libraries for AI/ML and AWS interaction.

• System Design: Prepare for questions on designing distributed systems, APIs, and data pipelines.

• Behavioral Scenarios: Prepare specific examples using the STAR method (Situation, Task, Action, Result) for common Amazon leadership principles like Customer Obsession, Ownership, and Bias for Action.

📝 Enhancement Note: The interview process at Amazon is rigorous and multi-faceted. A strong portfolio that clearly articulates technical solutions, the development process, and measurable outcomes is paramount. Candidates should be ready to demonstrate not just theoretical knowledge but practical application and problem-solving skills.

🛠 Tools & Technology Stack

Primary Tools:

• AWS AI Services: Amazon Bedrock, Amazon SageMaker (for model training, deployment, and MLOps), Amazon Comprehend, Amazon Rekognition, etc.

• Programming Languages: Python (primary for AI/ML), potentially Java, Node.js, or Go.

• AI/ML Frameworks: TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers.

• Agentic AI Frameworks: LangChain, LlamaIndex, or custom orchestration frameworks.

• Development Environments: IDEs like VS Code, PyCharm; Jupyter Notebooks.

Analytics & Reporting:

• AWS Services: Amazon CloudWatch (for monitoring and logging), AWS Cost Explorer.

• Data Visualization: Tools like Tableau, Power BI, or AWS QuickSight might be used for presenting prototype performance.

CRM & Automation:

• AWS Services: AWS Lambda (for serverless functions), AWS Step Functions (for workflow orchestration), Amazon API Gateway.

• Version Control: Git, GitHub/GitLab/Bitbucket.

• CI/CD: AWS CodePipeline, CodeBuild, CodeDeploy.

📝 Enhancement Note: Proficiency in the AWS ecosystem, especially its AI/ML suite, is non-negotiable. Experience with modern AI development patterns and frameworks like LangChain is highly desirable given the focus on Agentic AI. A strong software engineering foundation with Python and related libraries is also critical.

👥 Team Culture & Values

Operations Values:

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

• Bias for Action: Moving quickly, experimenting, and iterating to achieve breakthrough outcomes.

• Invent and Simplify: Developing novel solutions and making complex technologies accessible and easy to use.

• Ownership: Taking responsibility for the success of prototypes and customer engagements.

• Deliver Results: Focusing on tangible outcomes and demonstrating value through working solutions.

Collaboration Style:

• Cross-functional Integration: Close collaboration with field teams (Technical Account Managers, Solutions Architects), Product Managers, and other engineering teams within AWS.

• Iterative Feedback Loops: Continuous feedback exchange with customers and internal stakeholders to refine prototypes and strategies.

• Knowledge Sharing Culture: Actively contributing to internal documentation, best practices, and external content (blogs, whitepapers) to uplift the broader AWS community.

📝 Enhancement Note: The PACE team embodies Amazon's core leadership principles. Candidates should demonstrate a proactive, customer-focused approach, a willingness to innovate rapidly, and a collaborative spirit. The emphasis is on getting things done efficiently and effectively, with a strong focus on customer success and driving adoption of AWS AI technologies.

⚡ Challenges & Growth Opportunities

Challenges:

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

• Bridging Prototypes to Production: Translating experimental prototypes into robust, scalable, and secure production solutions on AWS demands careful architectural planning and execution.

• Customer Education & Adoption: Guiding diverse customers through complex AI concepts and adoption strategies, overcoming skepticism or technical hurdles.

• Balancing Speed and Quality: Delivering innovative prototypes quickly while ensuring they adhere to best practices and provide a solid foundation for future development.

Learning & Development Opportunities:

• Cutting-Edge AI Exposure: Direct engagement with the latest AI models, techniques, and AWS services, offering unparalleled learning opportunities.

• AWS Certifications: Opportunities to pursue advanced AWS certifications, particularly in AI/ML and Cloud Architecture.

• Internal Training & Workshops: Access to Amazon's extensive internal learning resources, training programs, and workshops focused on AI and cloud technologies.

• Industry Conferences: Potential to attend and present at major AI and cloud technology conferences, enhancing professional visibility and networking.

• Mentorship: Opportunities to learn from and mentor other engineers and architects within the AWS ecosystem.

📝 Enhancement Note: This role presents a unique opportunity to be at the forefront of AI innovation within a leading cloud provider. The challenges are significant but are directly tied to high-impact growth and learning opportunities.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex Generative AI or Agentic AI problem you've solved. What was your approach, what AWS services did you use, and what was the outcome?" (Focus on STAR method, technical depth, and impact).

• "How would you design an agentic system for [specific customer use case, e.g., customer support, internal knowledge retrieval]? What are the key components, potential challenges, and how would you ensure it's production-ready on AWS?" (Assess architectural thinking, AWS knowledge, and understanding of agent patterns).

• "Imagine a customer is hesitant about adopting LLMs due to security or cost concerns. How would you address their concerns and build trust using AWS services?" (Evaluate customer obsession, communication skills, and understanding of AWS security/cost management). Company & Culture Questions:

• "Why are you interested in Amazon and the PACE team specifically?" (Demonstrate understanding of Amazon's customer obsession, innovation culture, and the PACE team's mission).

• "Tell me about a time you had to invent and simplify a complex technical solution." (Relate to Amazon's leadership principles).

• "How do you stay current with the rapidly evolving field of AI/ML?" (Showcase continuous learning and passion for the domain). Portfolio Presentation Strategy:

• Concise Storytelling: For each project, deliver a clear narrative: problem, solution, your role, outcome, and lessons learned.

• Visual Aids: Use architecture diagrams, code snippets (if appropriate), and demo videos to illustrate your work.

• Technical Deep Dive: Be prepared to answer detailed questions about your design choices, technology stack, trade-offs, and implementation challenges.

• Quantify Impact: Whenever possible, present metrics that demonstrate the value and success of your prototypes.

• Focus on AWS: Highlight your proficiency and strategic use of relevant AWS AI/ML services.

📝 Enhancement Note: Preparation should focus on demonstrating practical experience with Generative AI, Agentic AI, and AWS. Candidates need to articulate their thought process, technical decisions, and the business impact of their work, aligning with Amazon's customer-centric and results-oriented culture.

📌 Application Steps

To apply for this Prototyping Architect position:

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

• Tailor Your Resume: Highlight your 10+ years of IT experience, focusing on software development, cloud computing (especially AWS), and your specific expertise in Generative AI, Agentic AI, and Machine Learning. Use keywords from the job description.

• Prepare Your Portfolio: Curate 2-3 key projects that showcase your AI/ML prototyping skills, AWS implementation experience, and ability to translate business needs into technical solutions. Be ready to present these in detail.

• Research Amazon's Leadership Principles: Familiarize yourself with principles like Customer Obsession, Bias for Action, Invent and Simplify, and Ownership, and prepare examples from your experience.

• Practice Technical Explanations: Rehearse explaining complex AI concepts, architectural designs, and your project outcomes clearly and concisely.

⚠️ 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.