Technical Program Manager III, AWS Prototyping and AI Customer Engineering (PACE)

📍 Job Overview

Job Title: Technical Program Manager III, AWS Prototyping and AI Customer Engineering (PACE)

Company: Amazon

Location: Toronto, Ontario, Canada

Job Type: Full-time

Category: Revenue Operations / Sales Operations / GTM Operations (Focus on Customer Engineering & AI Prototyping)

Date Posted: May 5, 2026

Experience Level: 10+ Years

Remote Status: On-site

🚀 Role Summary

• Orchestrate end-to-end AI-focused customer engagements, from concept to delivery, leveraging Amazon's Working Backwards methodology to accelerate adoption of Generative AI and Agentic AI solutions.

• Drive high-velocity prototype development by coordinating Prototyping Architects, Design Technologists, and customer stakeholders, ensuring rapid iteration and impactful business outcomes.

• Partner with regional Sales and Solutions Architecture teams to identify, qualify, and scope AI opportunities, directly contributing to AWS's global market penetration.

• Establish and track key performance indicators (KPIs) and engagement metrics to measure success, report on business impact, and drive continuous improvement in AI solution delivery.

📝 Enhancement Note: This role, while titled Technical Program Manager, has significant overlap with GTM Operations and Customer Engineering functions. The emphasis on accelerating customer adoption of cutting-edge AI technologies, partnering with sales, and driving prototype delivery aligns closely with operationalizing GTM strategies for new technologies.

📈 Primary Responsibilities

• Own the full lifecycle of strategic AI-focused customer engagements, from initial kick-off through successful delivery, ensuring alignment with the Working Backwards methodology for requirement definition and scope management.

• Facilitate the design and architecture of AI solutions, including RAG architectures and agentic patterns, by coordinating Prototyping Architects and Design Technologists, and driving the adoption of AI-driven development tools.

• Collaborate with regional AWS Sales and Solutions Architecture teams to identify and qualify promising 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 their quantifiable business impact.

• Define, implement, and monitor engagement metrics and delivery velocity KPIs to measure the success of AI prototyping efforts and contribute to broader program-level objectives.

• Develop and refine operational processes to enhance team efficiency, accelerate prototype delivery speed, and foster knowledge sharing across the global PACE community.

• Proactively identify potential risks inherent in AI implementations (e.g., data access, model bias, integration challenges) and develop robust mitigation strategies to ensure successful and responsible outcomes.

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

📝 Enhancement Note: The responsibilities highlight a strong need for operationalizing complex technical projects within a customer-facing GTM context. The emphasis on process development, risk mitigation specific to AI, and driving adoption through prototypes indicates a role that bridges technical execution with strategic GTM operations.

🎓 Skills & Qualifications

Education: Bachelor's degree in Engineering, Computer Science, or a related technical field.

Experience:

• Minimum of 6 years in Technical Program Management, encompassing scope, schedule, budget, quality, risk, and critical path management.

• Minimum of 7 years of direct experience working collaboratively with engineering teams.

• Proven experience in managing projects across cross-functional teams, developing sustainable operational processes, and coordinating complex release schedules.

Required Skills:

• Technical Program Management: Expertise in managing complex, multi-faceted technical projects from initiation to completion, with a strong emphasis on agile methodologies and iterative development.

• Generative AI & Agentic AI: Deep understanding of Generative AI concepts, Large Language Models (LLMs), Retrieval Augmented Generation (RAG) architectures, and autonomous agent principles.

• Customer Engagement & GTM Strategy: Ability to lead customer-facing engagements, understand business needs, and translate them into technical solutions that drive value and adoption.

• Process Development & Optimization: Skill in designing, implementing, and refining operational processes to improve efficiency, velocity, and knowledge sharing within technical teams.

• Stakeholder Management: Proven ability to build relationships and communicate effectively with both technical teams and executive-level stakeholders, managing expectations and driving consensus.

• Risk Management: Proactive identification and mitigation of technical and project risks, especially those unique to AI implementations.

Preferred Skills:

• Working Backwards Methodology: Practical application of Amazon's customer-centric product development framework.

• AI Solution Design: Experience architecting and designing AI-powered solutions tailored to specific business challenges.

• Product Thinking: Ability to approach AI prototypes with a product mindset, considering long-term value, user experience, and scalability.

• Data Analysis & KPI Definition: Experience in defining, tracking, and reporting on key metrics that demonstrate business impact and inform strategic decisions.

• Cloud Computing (AWS): Familiarity with the AWS ecosystem and its services relevant to AI/ML workloads.

📝 Enhancement Note: The "Preferred Qualifications" strongly suggest a need for candidates who can not only manage projects but also contribute operationally to the GTM strategy for AI products. The emphasis on KPIs, cross-functional management, and reporting to senior leadership are core GTM operations competencies. The "10+ Years" experience level inferred from AI analysis suggests this role requires seasoned professionals capable of strategic impact.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• AI Prototype Case Studies: Showcase successful AI prototyping projects, detailing the problem statement, the AI approach (e.g., LLM, RAG, agentic), the development process, and the resulting business impact or key learnings.

• Process Improvement Initiatives: Examples of implemented processes that enhanced team efficiency, accelerated project delivery, or improved collaboration, with quantifiable results.

• System Integration Contributions: Documentation or discussion of how you've facilitated the integration of new tools or technologies (especially AI-related) into existing workflows or customer environments.

• Risk Mitigation Frameworks: Evidence of developing and applying frameworks for identifying and mitigating risks, particularly within complex technical or AI projects.

Process Documentation:

• Workflow Design & Optimization: Demonstrate experience in mapping, analyzing, and optimizing complex workflows, particularly those involving multiple technical teams and customer interactions.

• Implementation & Automation Methods: Provide examples of how you've managed the implementation of new technical solutions or automated processes to drive efficiency and speed.

• Measurement & Performance Analysis: Showcase your ability to define success metrics, track performance against them, and use data to drive continuous improvement in processes and outcomes.

📝 Enhancement Note: For a role focused on accelerating AI adoption and prototyping, a portfolio demonstrating hands-on experience with AI technologies, process optimization for rapid delivery, and clear articulation of business impact through metrics is crucial. This goes beyond traditional TPM portfolios to highlight operationalizing innovation.

💵 Compensation & Benefits

Salary Range: $128,100 - $214,000 CAD annually.

Benefits:

• Comprehensive Health Insurance: Includes medical, dental, and vision coverage.

• Prescription Insurance: Coverage for prescription medications.

• Basic Life & AD&D Insurance: Essential life and accidental death and dismemberment coverage.

• Registered Retirement Savings Plan (RRSP): Company-sponsored retirement savings plan.

• Deferred Profit Sharing Plan (DPSP): Opportunity for profit sharing contributions.

• Paid Time Off (PTO): Generous paid time off for work-life balance.

• Additional Resources: Access to resources aimed at improving overall health and well-being.

Working Hours: Standard 40-hour work week.

📝 Enhancement Note: The provided salary range is specific to Toronto, Ontario, Canada. This range reflects a senior-level Technical Program Manager role within a major technology company like Amazon, considering the demand for AI expertise and GTM operational skills. The benefits package is typical for large, established tech organizations, offering robust support for employees.

🎯 Team & Company Context

🏢 Company Culture

Industry: Cloud Computing and Artificial Intelligence Services. Amazon Web Services (AWS) is a leader in providing comprehensive and widely adopted cloud computing services, with a significant and growing focus on AI/ML solutions.

Company Size: Amazon is a global technology giant, employing hundreds of thousands worldwide, indicating a large, complex, and resource-rich environment. This size offers significant opportunities for impact but also requires navigating established processes.

Founded: Amazon was founded in 1994, giving it a long history of innovation and customer obsession, which permeates its culture and operational methodologies.

Team Structure:

• The PACE (Prototyping and AI Customer Engineering) team operates within AWS Global Sales, focusing on accelerating customer adoption of AI technologies.

• This team likely includes Prototyping Architects, Design Technologists, and Technical Program Managers, working in close collaboration with regional Sales and Solutions Architecture teams.

Methodology:

• Working Backwards: A core Amazon methodology emphasizing starting with the customer and working backward to define requirements and build solutions. This is paramount for the PACE team.

• Data-Driven Decision Making: Emphasis on using metrics and KPIs to measure success, identify opportunities, and drive continuous improvement in delivery and customer impact.

• Agile & Iterative Development: The PACE team's mandate to build prototypes rapidly implies an agile, iterative approach to solution development and customer engagement.

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

📝 Enhancement Note: Amazon's culture is known for its customer obsession, long-term thinking, and high standards. For an AI/GTM role, understanding how the "Working Backwards" methodology is applied to nascent technologies like Generative AI is key. The scale of Amazon means operations professionals must be adept at driving change within a large, structured organization.

📈 Career & Growth Analysis

Operations Career Level: This is a Technical Program Manager III role, indicating a senior-level position. It requires significant experience in project and program management, with a specialization in emerging technologies like AI. The role involves end-to-end ownership of customer engagements, strategic input, and process improvement, placing it at a high level of individual contribution with potential for leadership influence.

Reporting Structure: The role reports into the AWS Prototyping and AI Customer Engineering (PACE) team, which is part of AWS Global Sales. The TPM III will likely report to a PACE leadership role (e.g., Program Manager Manager, Director) and will work closely with Sales, Solutions Architects, and customer stakeholders.

Operations Impact: The operations impact is significant, directly influencing the speed and success of AI adoption for AWS customers. By accelerating prototype delivery and refining engagement processes, this role contributes to:

• Revenue Growth: Enabling customers to adopt AWS AI services faster leads to increased consumption and revenue.

• Customer Success & Retention: Delivering impactful AI prototypes builds customer trust and loyalty, fostering long-term partnerships.

• Market Leadership: Establishing best practices and successful patterns for AI adoption positions AWS as a leader in the AI space.

• Innovation Acceleration: Facilitating rapid prototyping helps customers and AWS explore new AI use cases and drive innovation.

Growth Opportunities:

• Specialization in AI/ML: Deepen expertise in cutting-edge AI technologies, becoming a subject matter expert in Generative AI, Agentic AI, and related fields.

• Leadership Track: Progress into leadership roles within PACE or other AWS customer-facing organizations, managing larger programs or teams.

• Cross-Functional Mobility: Transition into roles within Product Management, Solutions Architecture, Sales Strategy, or other GTM operations functions within AWS.

• Industry Influence: Contribute to defining industry best practices for AI adoption and prototyping through internal knowledge sharing and potentially external contributions.

📝 Enhancement Note: This role offers a unique opportunity to be at the forefront of AI adoption, blending technical program management with strategic GTM operations. The growth trajectory is strong, with clear paths for technical specialization or leadership within a rapidly evolving field.

🌐 Work Environment

Office Type: This is an on-site role, indicating a traditional office environment in Toronto. This setting facilitates in-person collaboration, team building, and direct interaction with colleagues and potentially clients.

Office Location(s): Toronto, Ontario, Canada. This location offers access to a vibrant tech ecosystem and a diverse talent pool.

Workspace Context:

• Collaborative Environment: The role fosters collaboration with Prototyping Architects, Design Technologists, Sales, Solutions Architects, and customer teams. The office setting supports spontaneous interactions and structured working sessions.

• Tools & Technology: Access to Amazon's internal tools, cloud infrastructure (AWS), and specialized AI development platforms will be standard. Teams are expected to leverage these to their full potential.

• Team Interaction: Regular team meetings, syncs, and retrospectives will be part of the operational rhythm, ensuring alignment and knowledge sharing within the PACE community.

Work Schedule: The standard 40-hour work week applies. However, given the fast-paced nature of AI prototyping and customer engagements, some flexibility may be required to meet critical deadlines or customer needs. The role involves approximately 25% domestic travel (within Canada/USA) and 10% international travel, primarily to the USA, for customer engagements.

📝 Enhancement Note: The on-site requirement in Toronto, combined with significant travel, suggests a role that values in-person collaboration for complex problem-solving while requiring adaptability for client-facing activities. This blend is common for roles that bridge internal technical teams with external customer needs.

📄 Application & Portfolio Review Process

Interview Process:

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

• Hiring Manager Interview: A discussion focused on your experience with technical program management, AI technologies, customer engagements, and alignment with

Amazon's principles.

• Technical Deep Dive / Bar Raiser Interview: This stage typically involves multiple interviews with peers and senior leaders. Expect questions on:

  • AI Concepts & Application: Your understanding of Generative AI, LLMs, RAG, agentic patterns, and how to apply them to business problems.
  • Program Management Skills: Detailed scenarios testing your approach to scope, schedule, risk, and stakeholder management for complex technical projects.
  • Customer Engagement Scenarios: How you would approach a new customer engagement, define requirements using Working Backwards, and manage client expectations.
  • Process Improvement: Examples of how you've identified and solved operational inefficiencies.
  • Behavioral Questions: Using the STAR method (Situation, Task, Action, Result) to assess how you've handled specific work situations, aligned with Amazon's Leadership Principles.

• Portfolio Review: Be prepared to present 2-3 key projects from your portfolio that best demonstrate your capabilities in technical program management, AI, process optimization, and customer impact.

• Final Round: May involve executive-level interviews or a final "Bar Raiser" interview to ensure high standards are met.

Portfolio Review Tips:

• Quantify Impact: For each project, clearly articulate the business problem, your role, the solution implemented, and the quantifiable results (e.g., % improvement in delivery speed, % reduction in risk, customer satisfaction scores, adoption metrics).

• Focus on AI Relevance: Highlight projects involving AI, machine learning, or complex technical systems, showcasing your understanding of these domains.

• Demonstrate Process Thinking: Show examples of how you've improved processes, managed dependencies, or driven efficiency.

• Structure for Clarity: Organize your portfolio logically. For each case study, follow a clear narrative: problem, your approach, execution, results, and lessons learned.

• Tailor to PACE: Emphasize your experience with prototyping, rapid development cycles, and customer-facing consultative roles.

Challenge Preparation:

• AI Use Case Brainstorming: Be ready to quickly brainstorm potential AI use cases for various industries or specific business problems.

• Working Backwards Simulation: Practice formulating a "Working Backwards" document for a hypothetical AI product or service.

• Risk Identification & Mitigation: Prepare to identify potential risks in an AI project scenario and propose mitigation strategies.

• Process Improvement Scenarios: Think about common operational bottlenecks in technical program management or AI development and how you would address them.

📝 Enhancement Note: The interview process at Amazon is known for its rigor and focus on leadership principles. For a TPM role in a cutting-edge area like AI, candidates must demonstrate not only technical project management skills but also a deep understanding of AI, customer focus, and operational excellence. The portfolio is a critical tool for demonstrating these capabilities.

🛠 Tools & Technology Stack

Primary Tools:

• Project Management Software: Proficiency in tools like Jira, Confluence, Asana, or similar for task tracking, sprint planning, and documentation.

• Collaboration Platforms: Microsoft Teams, Slack, or Amazon's internal communication tools for real-time communication and team coordination.

• Presentation Tools: PowerPoint, Google Slides, or similar for creating and delivering presentations to stakeholders and customers.

Analytics & Reporting:

• Business Intelligence Tools: Experience with tools like Tableau, Power BI, or Amazon QuickSight for creating dashboards and visualizing key metrics.

• Data Analysis Tools: Familiarity with SQL for data querying and potentially Python or R for more advanced data analysis related to project performance or AI model evaluation.

• Spreadsheet Software: Advanced proficiency in Microsoft Excel or Google Sheets for data manipulation, tracking, and reporting.

CRM & Automation:

• CRM Systems: Experience with Salesforce or similar CRM platforms to understand customer data, sales pipelines, and account management processes.

• Cloud Platforms: Deep familiarity with AWS services relevant to AI/ML, such as Amazon SageMaker, Amazon Bedrock, EC2, S3, Lambda, and potentially others related to data processing and analytics.

• Automation Tools: Understanding of how to leverage automation for workflows, reporting, and development processes, potentially involving scripting (e.g., Python) or workflow automation platforms.

📝 Enhancement Note: This role requires a strong command of standard project management and collaboration tools, coupled with deep expertise in AWS services, particularly those related to AI/ML. The ability to leverage data analytics and CRM insights to inform program strategy and customer engagement is also critical.

👥 Team Culture & Values

Operations Values:

• Customer Obsession: A foundational Amazon principle; all actions and decisions must start with understanding and meeting customer needs through effective AI solutions.

• Bias for Action: Encouraging rapid execution and iteration, particularly crucial in the fast-paced AI prototyping environment. Decisions are made quickly, and progress is prioritized.

• Invent and Simplify: Continuously seeking innovative solutions and simplifying complex problems to drive efficiency and deliver value to customers and the team.

• Ownership: Taking full responsibility for engagements, outcomes, and processes, demonstrating accountability for success.

• Deliver Results: Focusing on achieving measurable outcomes and driving business impact through AI prototypes and optimized operations.

Collaboration Style:

• Cross-Functional Integration: Expect a highly collaborative environment where close partnerships with Sales, Solutions Architecture, and technical teams are essential for success.

• Constructive Conflict: Healthy debate and differing viewpoints are encouraged to arrive at the best solutions, especially when navigating the complexities of AI.

• Knowledge Sharing: A culture of sharing learnings, best practices, and insights across the PACE community to accelerate collective growth and efficiency.

• Data-Informed Discussions: Discussions and decisions are expected to be backed by data and metrics, fostering a pragmatic and results-oriented approach.

📝 Enhancement Note: Amazon's core leadership principles are deeply ingrained in its culture. For this role, demonstrating alignment with customer obsession, bias for action, and ownership will be critical for fitting into the PACE team's operational ethos.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapidly Evolving AI Landscape: Staying abreast of the latest advancements in Generative AI, LLMs, and agentic technologies, and quickly applying them to customer use cases.

• Ambiguity in AI Solutions: Many AI problems lack predefined solutions, requiring a high tolerance for ambiguity and a creative approach to problem-solving.

• Balancing Speed and Quality: Accelerating prototype delivery while ensuring responsible AI practices, robustness, and potential for production readiness.

• Cross-Functional Alignment: Effectively coordinating diverse teams (Sales, Engineering, Customer) with potentially different priorities and timelines.

• Scalability of Prototyping: Developing processes that allow for scaling successful prototypes into broader customer solutions or product offerings.

Learning & Development Opportunities:

• Cutting-Edge AI Exposure: Direct involvement with the latest AI technologies and their application in real-world business scenarios.

• Advanced Technical Program Management: Hone skills in managing highly complex, innovative projects with a focus on emerging technologies.

• Mentorship: Access to experienced leaders within AWS and Amazon who can provide guidance on career development, technical expertise, and leadership skills.

• Industry Conferences & Training: Opportunities to attend relevant industry events, workshops, and training programs focused on AI and cloud computing.

• Leadership Development: Potential for growth into formal leadership roles, managing teams or larger strategic initiatives within the AI customer engineering space.

📝 Enhancement Note: The challenges presented are inherent to working with cutting-edge AI. The growth opportunities reflect the strategic importance of this role and the potential for significant career advancement within Amazon's AI and cloud divisions.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you had to manage a complex technical program with significant ambiguity. How did you define success criteria and navigate trade-offs?" (Tests PM skills, ambiguity handling)

• "How would you apply Amazon's Working Backwards methodology to develop a new AI prototype for a customer in the [specific industry, e.g., retail] sector?" (Tests methodology application, customer focus)

• "Walk me through a process you designed or significantly improved. What was the problem, your solution, and the measurable impact?" (Tests process improvement, operational thinking)

Company & Culture Questions:

• "What excites you most about working with Generative AI and Agentic AI at AWS?" (Tests passion, alignment with role)

• "How do you handle disagreements with stakeholders or team members regarding technical direction or project priorities?" (Tests collaboration, conflict resolution)

• "Tell me about a time you had to deliver results under tight deadlines. How did you prioritize and manage your time?" (Tests bias for action, delivery focus)

Portfolio Presentation Strategy:

• "Working Backwards" Narrative: Structure your case studies by starting with the customer problem or business need, then detailing your role, the solution (especially the AI component), the execution process, and finally, the quantifiable results and learnings.

• Highlighting AI Expertise: Clearly articulate the specific AI technologies used (LLMs, RAG, agents) and why they were the right choice for the problem.

• Demonstrating Operational Excellence: Emphasize how you managed scope, schedule, risks, and cross-functional dependencies. Showcase any process improvements you implemented.

• Quantifiable Business Impact: For each project, be ready to discuss metrics related to customer adoption, efficiency gains, cost savings, revenue impact, or other relevant business outcomes.

• Conciseness and Clarity: Aim for presentations that are clear, concise, and impactful, typically 5-10 minutes per case study.

📝 Enhancement Note: Amazon interviews are rigorous and designed to assess both technical capability and alignment with leadership principles. For this role, candidates must be prepared to articulate their experience with AI, program management, customer engagement, and process optimization, using concrete examples and quantifiable results.

📌 Application Steps

To apply for this operations position:

• Submit your application through the official Amazon Jobs portal.

• Customize Your Resume: Tailor your resume to highlight experience relevant to Technical Program Management, Generative AI, Agentic AI, cloud computing (AWS), customer engagement, and process optimization. Use keywords from the job description.

• Prepare Your Portfolio: Curate 2-3 strong case studies that showcase your ability to manage complex technical programs, drive AI initiatives, improve operational processes, and deliver measurable business results. Ensure these examples align with the PACE team's focus on rapid prototyping and customer impact.

• Practice STAR Method: Rehearse responses to common behavioral questions using the STAR method, focusing on demonstrating Amazon's Leadership Principles.

• Research AWS PACE: Understand the PACE team's mission, the types of AI prototypes they build, and how they contribute to AWS's overall GTM strategy. Familiarize yourself with Amazon's Working Backwards methodology.

• Prepare Questions: Develop thoughtful questions to ask the interviewers about the team, the role, current challenges, and future opportunities.

⚠️ 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.