Senior Solutions Developer, Prototyping AI Customer Engineering (PACE)

📍 Job Overview

Job Title: Senior Solutions Developer, Prototyping AI Customer Engineering (PACE)

Company: Amazon

Location: Austin, Texas, United States; Seattle, Washington, United States; Arlington, Virginia, United States; New York, New York, United States; Herndon, Virginia, United States; Chicago, Illinois, United States; San Francisco, California, United States; Dallas, Texas, United States

Job Type: Full-Time

Category: Customer Engineering / Solutions Development

Date Posted: 2026-08-25

Experience Level: 10+ Years

Remote Status: Hybrid/Remote Options Available

🚀 Role Summary

• Spearhead the design and development of innovative technical prototypes, with a strong emphasis on Generative AI and emerging technologies within the AWS ecosystem.

• Act as a trusted customer advocate, collaborating closely with enterprise clients to understand their unique challenges and translate them into tangible, high-quality technical solutions.

• Drive the adoption of AWS services by crafting and implementing proof-of-concepts and early-stage product iterations across diverse industry verticals.

• Serve as a technical leader, combining deep development expertise with architectural understanding to guide small, agile teams through rapid prototyping cycles.

📝 Enhancement Note: This role is positioned within Amazon's Prototyping AI Customer Engineering (PACE) team, indicating a focus on cutting-edge AI/ML solutions and rapid iteration for enterprise clients. The "Senior" title, combined with the 10+ years of experience requirement, suggests a need for significant technical leadership and customer-facing consulting experience. The emphasis on "builder's mentality" and "hands-on" work points to a role that requires both strategic thinking and deep technical execution.

📈 Primary Responsibilities

• Engage with enterprise customers to deeply understand their specific use cases, desired business outcomes, and technical requirements for AI-driven solutions.

• Lead the design and implementation of technical prototypes, leveraging AWS services and emerging technologies, within aggressive 4-6 week development cycles.

• Develop production-ready code, AWS Cloud Development Kit (CDK) infrastructure as code, and comprehensive documentation for delivered prototypes.

• Collaborate with cross-functional teams, including Customer Success Managers and AWS specialists, to ensure successful prototype delivery and customer satisfaction.

• Identify and champion opportunities to scale the impact of prototype work through internal enablement initiatives, external-facing content, and best practice sharing.

• Mentor junior developers and team members, fostering a culture of knowledge sharing, technical excellence, and continuous learning.

• Stay abreast of the latest advancements in AI/ML, agentic design, serverless computing, IoT, and other relevant technologies, proactively experimenting with new approaches.

• Contribute to the continuous improvement of prototyping methodologies, tools, and processes within the PACE team.

📝 Enhancement Note: The core responsibility involves rapid prototyping for enterprise clients, a critical function in GTM (Go-To-Market) strategies for new technologies like Generative AI. This requires not just coding skills but also strong problem-solving, communication, and customer engagement capabilities, aligning with a senior-level technical consultant role.

🎓 Skills & Qualifications

Education: While no specific degree is mandated, a strong technical background equivalent to a Bachelor's or Master's degree in Computer Science, Engineering, or a related field is implied by the experience requirements.

Experience:

• Minimum of 10 years of IT development, implementation, or consulting experience within the software or internet industries.

• Minimum of 8 years of experience in specific technology domain areas such as software development, cloud computing, systems engineering, infrastructure, security, networking, or data & analytics. Required Skills:

• Proven expertise in cloud architecture, particularly within the AWS ecosystem.

• Deep understanding and hands-on experience with Generative AI concepts, models, and applications.

• Proficiency in software development with a strong emphasis on delivering high-quality, enterprise-scale solutions.

• Experience in prototyping and rapid development cycles, demonstrating the ability to quickly build and validate technical solutions.

• Demonstrated ability to lead technical projects and mentor other developers.

• Strong customer advocacy and consulting skills, with experience engaging directly with enterprise clients.

• Familiarity with system development lifecycle (SDLC) best practices.

• Excellent communication and interpersonal skills, capable of explaining complex technical concepts to both technical and non-technical audiences. Preferred Skills:

• 10+ years of experience in infrastructure architecture, database architecture, and networking.

• Experience designing, building, deploying, and operating agentic solutions using multi-agent orchestration frameworks.

• Hands-on experience with serverless architectures and IoT platforms.

• Experience with AWS CDK or other infrastructure-as-code tools.

• Familiarity with AR/VR/Spatial computing technologies.

• Experience in various industry verticals such as automotive, energy, healthcare, telco, manufacturing, or media and entertainment.

📝 Enhancement Note: The emphasis on "8+ years of specific technology domain areas" and "10+ years of IT development or implementation/consulting" clearly signals a senior-level role requiring seasoned professionals. The preferred qualifications highlight a growing demand for expertise in agentic AI and related advanced technologies, crucial for future-facing GTM strategies.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrable examples of successfully developed technical prototypes, particularly those involving AI/ML or cloud-native solutions.

• Case studies showcasing the end-to-end process of taking a customer's use case from initial concept to a functional prototype.

• Evidence of contributions to system architecture, infrastructure design, or complex software development projects.

• Documentation or examples highlighting the application of cloud development kit (CDK) or similar Infrastructure as Code (IaC) methodologies.

• Examples of how you've technically validated and proved the feasibility of novel solutions or emerging technologies. Process Documentation:

• Showcase an ability to document complex technical processes, including development workflows, architectural decisions, and deployment strategies.

• Provide examples of how you've contributed to or established efficient development processes for rapid prototyping or agile development environments.

• Evidence of understanding and applying metrics to measure the success and impact of prototypes and developed solutions.

📝 Enhancement Note: For a "Solutions Developer" role focused on prototyping, a portfolio is critical. It should not only showcase completed projects but also the process behind them – how problems were understood, solutions architected, and prototypes built and validated. This is a key differentiator for operations and engineering roles where impact and methodology are paramount.

💵 Compensation & Benefits

Salary Range:

• San Francisco, California: $176,600 - $239,000 USD annually

• Chicago, Illinois: $153,600 - $207,800 USD annually

• New York, New York: $169,000 - $228,600 USD annually

• Austin, Texas: $153,600 - $207,800 USD annually

• Dallas, Texas: $153,600 - $207,800 USD annually

• Arlington, Virginia: $153,600 - $207,800 USD annually

• Herndon, Virginia: $153,600 - $207,800 USD annually

• Seattle, Washington: $153,600 - $207,800 USD annually

Benefits:

• Comprehensive health insurance package including medical, dental, and vision coverage.

• Prescription drug coverage and options for supplemental life insurance.

• Employee Assistance Program (EAP) and dedicated mental health support.

• Access to a Medical Advice Line for health consultations.

• Flexible Spending Accounts (FSAs) for healthcare and dependent care expenses.

• Adoption and Surrogacy Reimbursement coverage.

• 401(k) retirement savings plan with company matching.

• Generous Paid Time Off (PTO) and parental leave policies.

• Additional compensation in the form of sign-on payments and Restricted Stock Units (RSUs).

Working Hours: Standard full-time work schedule, typically around 40 hours per week, with flexibility expected based on project demands and customer needs.

📝 Enhancement Note: The salary ranges provided are competitive for senior-level technical roles in these major US tech hubs. The inclusion of sign-on bonuses and RSUs indicates a comprehensive total compensation package common in large tech organizations like Amazon. The benefits package is extensive, covering health, wellness, financial planning, and family support, reflecting Amazon's commitment to employee well-being.

🎯 Team & Company Context

🏢 Company Culture

Industry: Cloud Computing, Artificial Intelligence, E-commerce, Technology Services. Amazon Web Services (AWS) is a global leader in cloud computing, providing a vast array of services that power businesses worldwide. The company operates at the forefront of technological innovation, with a significant focus on AI/ML.

Company Size: Amazon is a massive global corporation, employing over 1.5 million people worldwide. This scale provides unparalleled opportunities for career growth, access to resources, and exposure to diverse projects. For operations professionals, this means working within a highly structured yet dynamic environment with sophisticated internal processes and cutting-edge technology.

Founded: Amazon was founded in 1994 by Jeff Bezos. AWS was launched in 2006, revolutionizing the cloud computing landscape. This history signifies a culture of innovation, long-term vision, and a relentless pursuit of customer satisfaction.

Team Structure:

• The Prototyping AI Customer Engineering (PACE) team is a specialized group within AWS Customer Engineering.

• It comprises experienced technologists and developers who act as customer advocates.

• The team operates with a "small team of developers" model for prototyping engagements, suggesting agile, cross-functional units formed on a project basis.

• Collaboration extends across the US and EMEA regions, indicating a global and distributed team environment.

• Mentorship and knowledge sharing are integral to the team's structure, supporting career development. Methodology:

• Data-Driven Prototyping: Emphasis on understanding customer use cases and leveraging data to inform prototype design and validation.

• Agile Development: Rapid iteration cycles (4-6 weeks) for prototype delivery, requiring efficient workflows and quick adaptation.

• Customer-Centric Approach: Prototypes are built with specific customer needs and business value in mind.

• Technology Exploration: A continuous drive to learn and experiment with new technologies, especially in AI/ML and cloud-native services.

• Collaborative Problem-Solving: Working closely with customers and internal AWS teams to overcome technical challenges.

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

📝 Enhancement Note: Amazon's culture is known for its "Day 1" mentality, customer obsession, bias for action, and frugality. For operations roles, this translates to a fast-paced environment where efficiency, data-driven decisions, and a proactive approach are highly valued. The PACE team specifically embodies innovation and rapid execution.

📈 Career & Growth Analysis

Operations Career Level: This role represents a Senior Solutions Developer position, falling under the umbrella of Technical Engineering or Customer Engineering within AWS. It requires seasoned professionals capable of independent work, technical leadership, and direct customer engagement. The scope involves architecting and building cutting-edge solutions, often in uncharted technical territories.

Reporting Structure: While specific reporting lines aren't detailed, Senior Solutions Developers typically report to Engineering Managers, Principal Engineers, or Directors within the Customer Engineering or Professional Services organizations. They would work collaboratively within project-based teams, often interfacing with Sales, Solutions Architects, and Product Management.

Operations Impact: The impact of this role is significant and multifaceted:

• Customer Success: Directly enabling customers to leverage AWS and AI technologies to achieve their business objectives through functional prototypes.

• Technology Adoption: Accelerating the adoption of new AWS services and AI capabilities by demonstrating their practical application and value.

• Product Development: Providing critical early feedback and use-case validation that can inform future AWS product development.

• Revenue Enablement: Supporting sales cycles by providing technical validation and compelling demonstrations for potential clients.

Growth Opportunities:

• Technical Specialization: Deepen expertise in specific AI domains like Generative AI, agentic systems, or serverless architectures, becoming a recognized subject matter expert.

• Leadership Development: Progress into roles such as Principal Solutions Architect, Engineering Manager, or Program Manager, leading larger teams and initiatives.

• Cross-Functional Mobility: Opportunity to move into product management, solutions architecture, or even sales engineering roles within AWS.

• Continuous Learning: Access to extensive AWS training, certifications, and internal knowledge-sharing platforms to stay at the cutting edge of cloud technology.

• Global Exposure: Work on projects with diverse international clients, expanding global perspective and network.

📝 Enhancement Note: The career path for a Senior Solutions Developer at Amazon is typically one of deep technical specialization or a transition into broader leadership and architectural roles. The emphasis on mentorship suggests a desire to build future leaders within the organization.

🌐 Work Environment

Office Type: This role offers a hybrid work model. While specific office locations are listed (Austin, Seattle, Arlington, New York, Herndon, Chicago, San Francisco, Dallas), the job description implies flexibility, allowing individuals to work remotely on certain days or entirely, depending on project needs and team agreements. Amazon emphasizes a culture of "Work hard. Have fun. Make history."

Office Location(s): The role is available in major tech hubs across the United States, including:

• San Francisco, California

• Chicago, Illinois

• New York, New York

• Austin, Texas

• Dallas, Texas

• Arlington, Virginia

• Herndon, Virginia

• Seattle, Washington

These locations provide access to vibrant tech communities and Amazon's extensive office infrastructure.

Workspace Context:

• Collaborative Environment: Expect to work in dynamic, team-oriented settings, whether in-person or virtually. The role requires close collaboration with customers, fellow developers, and AWS specialists.

• Cutting-Edge Tools: Access to a wide range of AWS services, development tools, and potentially specialized hardware for AI/ML prototyping.

• Knowledge Sharing: Opportunities to engage in team meetings, internal forums, and workshops designed for sharing insights and best practices related to AI and cloud development.

• Mentorship: A supportive environment where experienced professionals guide and mentor newer team members.

Work Schedule: While a standard 40-hour work week is typical, the fast-paced nature of prototyping and customer engagements may require flexibility. The emphasis on "self-guided and disciplined in managing your time" suggests autonomy and the need for effective personal time management to meet project deadlines.

📝 Enhancement Note: The hybrid/remote flexibility for this role is a significant draw, allowing professionals to balance personal needs with project demands. The listed locations are prime tech hubs, offering access to talent pools and industry events.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will review your application and resume, focusing on experience with AWS, AI/ML, software development, and customer-facing roles.

• Technical Phone Screen: Expect a call with an engineer or Solutions Architect to discuss your technical background, specific projects, and problem-solving approach.

This may include conceptual questions about cloud architecture, AI/ML, and system design.

• On-site/Virtual Loop: This typically involves multiple interviews (4-6 sessions) with various team members, including engineers, managers, and potentially peer developers. Interviews will likely cover:

  • Deep Technical Dive: In-depth discussions about past projects, architectural decisions, coding challenges, and hands-on experience with relevant technologies.
  • Customer Scenario/Case Study: You may be presented with a customer use case and asked to outline a prototyping approach, design considerations, and implementation steps.
  • Behavioral Questions: Standard Amazon Leadership Principles questions designed to assess your fit with the company culture (e.g., Customer Obsession, Bias for Action, Dive Deep, Invent and Simplify).
  • Portfolio Presentation: You will likely be asked to present one or more key projects from your portfolio, explaining the problem, your solution, the technology used, and the impact.

• Hiring Manager Interview: A final discussion to assess overall fit, career aspirations, and confirm alignment with the team's needs.

Portfolio Review Tips:

• Curate Selectively: Choose 2-3 of your most impactful projects that directly align with the role's requirements (AI/ML, cloud, prototyping, enterprise solutions).

• Structure Your Narrative: For each project, clearly articulate:

  • The customer's problem/use case.
  • Your specific role and contributions.
  • The technical challenges faced and how you overcame them.
  • The technologies and AWS services used.
  • The outcome, impact, and any quantifiable results (e.g., performance improvements, cost savings, adoption metrics).

• Highlight Process: Emphasize your methodology – how you approached design, development, testing, and customer engagement.

• Demonstrate Technical Depth: Be prepared to dive deep into the technical details of your projects, explain architectural choices, and discuss trade-offs.

• Showcase AI/ML Focus: If possible, include projects demonstrating expertise in Generative AI, machine learning models, or agentic systems.

• Prepare for Live Coding/Whiteboarding: Be ready to write code snippets or sketch system designs on a whiteboard (physical or virtual) to illustrate your thought process.

Challenge Preparation:

• AWS Fundamentals: Brush up on core AWS services (EC2, S3, Lambda, IAM, VPC) and services relevant to AI/ML (SageMaker, Bedrock, Rekognition, Comprehend).

• AI/ML Concepts: Review key concepts in machine learning, deep learning, natural language processing (NLP), and specifically Generative AI. Understand model training, deployment, and inference.

• System Design: Practice designing scalable, reliable, and performant systems on AWS, considering factors like microservices, data storage, and API design.

• Amazon Leadership Principles: Prepare specific examples for each principle, focusing on how you've demonstrated them in past roles. Use the STAR method (Situation, Task, Action, Result).

• Coding Proficiency: Ensure you are comfortable coding in at least one relevant language (e.g., Python, Java, Node.js) and can write clean, efficient code.

📝 Enhancement Note: Amazon's interview process is known for its rigor and focus on Leadership Principles. Candidates must be prepared for deep technical dives and behavioral assessments that probe their alignment with Amazon's core values. A well-prepared portfolio presentation is crucial for demonstrating practical skills and impact.

🛠 Tools & Technology Stack

Primary Tools:

• AWS Services: Extensive use of the AWS platform, including but not limited to:

  • Compute: EC2, Lambda, ECS, EKS
  • Storage: S3, EBS, RDS, DynamoDB
  • AI/ML: Amazon SageMaker, Amazon Bedrock, Rekognition, Comprehend, Translate, Transcribe, Lex.
  • Networking: VPC, Route 53, ELB
  • Developer Tools: AWS CDK, CloudFormation, CodeCommit, CodeBuild, CodeDeploy, CodePipeline.

• Programming Languages: Python (highly probable for AI/ML), Node.js, Java, Go.

• Containerization: Docker, Kubernetes (EKS).

• Infrastructure as Code (IaC): AWS CDK, CloudFormation.

Analytics & Reporting:

• AWS CloudWatch: For monitoring, logging, and performance analysis.

• AWS X-Ray: For tracing requests and debugging distributed applications.

• Potentially other BI/Data Visualization tools depending on specific project needs. CRM & Automation:

• While not a direct CRM role, understanding how prototypes integrate with customer workflows and systems is key.

• API Gateways: For managing and securing APIs.

• Event-driven architectures: Utilizing services like SQS, SNS, EventBridge for asynchronous communication and automation.

📝 Enhancement Note: Proficiency across the AWS ecosystem is paramount. The inclusion of "Agentic solutions" and "multi-agent orchestration" points towards advanced AI development, likely involving tools and frameworks beyond standard cloud services. Amazon Bedrock is a key service for Generative AI, so familiarity with it would be a significant advantage.

👥 Team Culture & Values

Operations Values:

• Customer Obsession: Deep understanding and commitment to meeting customer needs and delivering exceptional value through prototypes.

• Invent and Simplify: Continuously seeking innovative solutions and simplifying complex technical challenges.

• Bias for Action: Proactively taking initiative, making decisions quickly, and driving projects forward, especially in fast-paced prototyping environments.

• Dive Deep: Thoroughly analyzing customer requirements, technical details, and potential solutions to ensure high-quality outcomes.

• Ownership: Taking responsibility for the success of prototypes from conception through delivery.

• Learn and Be Curious: A strong desire to learn new technologies, experiment with emerging trends (especially in AI), and continuously develop skills.

Collaboration Style:

• Cross-functional Integration: Working seamlessly with customer teams, solutions architects, product managers, and other AWS specialists to achieve shared goals.

• Open Communication: Fostering an environment where ideas can be freely shared, debated, and refined.

• Knowledge Sharing: Actively participating in team meetings, internal forums, and documentation efforts to disseminate learnings and best practices.

• Mentorship: A culture where senior members guide and support the development of junior team members, fostering collective growth.

📝 Enhancement Note: Amazon's 16 Leadership Principles are deeply embedded in its culture. For this role, Customer Obsession, Invent and Simplify, Bias for Action, Dive Deep, Ownership, and Learn and Be Curious are particularly relevant to the daily execution of prototyping and customer engagement.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapid Iteration: The 4-6 week prototyping cycle demands extreme efficiency, adaptability, and the ability to manage multiple complex tasks simultaneously under tight deadlines.

• Technical Ambiguity: Working with cutting-edge technologies like Generative AI often means navigating undefined problems and exploring uncharted technical territory.

• Diverse Customer Needs: Catering to a wide range of industry verticals and customer maturity levels requires flexibility in technical approach and communication style.

• Staying Current: The pace of innovation in AI/ML necessitates continuous learning and skill development to remain effective.

• Balancing Innovation with Practicality: Ensuring that prototypes are not only technically advanced but also deliver tangible business value and are feasible for future adoption.

Learning & Development Opportunities:

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

• Internal Training & Workshops: Access to a wealth of internal training resources, workshops, and deep-dive sessions on new AWS services and technologies.

• Industry Conferences: Potential to attend leading AI/ML and cloud computing conferences to stay abreast of industry trends.

• Mentorship Programs: Formal and informal mentorship opportunities with senior leaders and subject matter experts within AWS.

• Project Variety: Exposure to a wide array of customer use cases and industry challenges, providing diverse learning experiences and skill development.

📝 Enhancement Note: This role is ideal for individuals who thrive in dynamic, challenging environments and are passionate about pushing the boundaries of technology. The growth opportunities are substantial, offering pathways for deep technical specialization or broader leadership roles within Amazon.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you had to prototype a complex AI/ML solution with limited information. What was your approach, and what were the key challenges?" (Focus on your methodology, problem-solving, and ability to handle ambiguity).

• "How would you approach designing a Generative AI solution for a customer in the [specific industry, e.g., healthcare] sector, considering data privacy and ethical implications?" (Demonstrate understanding of domain-specific challenges and responsible AI practices).

• "Walk me through a challenging customer engagement where you had to build trust and deliver a technical solution under pressure. How did you manage stakeholder expectations?" (Highlight communication, customer obsession, and bias for action).

• "Imagine you've identified a significant opportunity to improve a customer's workflow using a novel AWS service. How would you build a compelling prototype to demonstrate its value?" (Showcase innovation, simplification, and impact-driven development). Company & Culture Questions:

• "Why Amazon, and specifically why the PACE team?" (Align your motivations with Amazon's customer obsession, innovation, and the team's focus on cutting-edge AI).

• "How do you embody the Amazon Leadership Principle of 'Invent and Simplify' in your work?" (Provide a concrete example of developing an innovative solution and making it accessible).

• "Describe a situation where you had to 'Dive Deep' to understand a complex technical problem or customer requirement." (Focus on your analytical process and thoroughness).

• "How do you ensure you maintain a healthy work-life balance while working on demanding projects?" (Address Amazon's emphasis on work-life harmony and your personal strategies). Portfolio Presentation Strategy:

• Select Your Strongest Case Study: Choose a project that best showcases your skills in AI/ML, cloud development, and customer engagement.

• Outline Your Narrative: Structure your presentation logically: Problem -> Solution -> Your Role & Actions -> Technology Stack -> Results & Impact.

• Quantify Impact: Use data and metrics whenever possible to demonstrate the value of your work (e.g., performance improvements, cost savings, user adoption rates).

• Be Prepared for Deep Dives: Anticipate technical questions about your architectural choices, code implementation, and any challenges faced.

• Highlight Collaboration: Emphasize how you worked with customers and internal teams to achieve the outcome.

• Tailor to PACE: Frame your experience in the context of rapid prototyping and customer engineering, highlighting your ability to deliver quickly and effectively.

📝 Enhancement Note: Preparing specific examples using the STAR method for Amazon's Leadership Principles is crucial. For the portfolio presentation, focus on demonstrating not just what you built, but how you built it and the impact it had, aligning with Amazon's data-driven and results-oriented culture.

📌 Application Steps

To apply for this Senior Solutions Developer position at Amazon:

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

• Tailor your resume: Highlight your experience with AWS, Generative AI, machine learning, software development, cloud architecture, and customer-facing roles. Use keywords from the job description.

• Curate your portfolio: Select 2-3 key projects that demonstrate your ability to prototype complex AI/ML solutions for enterprise clients. Prepare a concise narrative for each, focusing on problem, solution, impact, and your specific contributions.

• Prepare for interviews: Thoroughly review AWS services, AI/ML concepts, system design principles, and Amazon's Leadership Principles. Practice answering behavioral questions using the STAR method and prepare for technical deep dives.

• Research Amazon and PACE: Understand Amazon's culture, values, and the specific mission of the Prototyping AI Customer Engineering (PACE) team to articulate your interest and alignment effectively.

⚠️ 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.