Full-Stack AI SaaS Development Team – Amazon PPC Automation, Data Engineering & AI Intelligence

Full-Stack AI SaaS Development Team – Amazon PPC Automation, Data Engineering & AI Intelligence Project Overview We are looking for a highly experienced individual or established development team to build an end-to-end AI-powered Amazon PPC automation and optimization SaaS platform. Our vision is to develop an intelligent software product that simplifies and automates Amazon advertising management. Sellers should be able to connect their Amazon accounts, launch campaigns, monitor performance, receive intelligent recommendations and automate advertising decisions without needing to become PPC experts. This is a serious SaaS development project with long-term potential. We are looking for professionals who have extensive hands-on experience building scalable SaaS platforms, integrating advertising APIs, developing data infrastructure, implementing AI systems and delivering production-ready software. We need a team capable of handling the entire technical production of this project, from initial architecture and infrastructure through development, testing, deployment and ongoing improvements. 1. Amazon PPC Account Integration & Campaign Creation The platform must provide a complete Amazon PPC management workflow. Responsibilities include: * Integrating Amazon Ads API and relevant Amazon Seller Partner API services. * Implementing secure OAuth authentication and Amazon account connections. * Supporting multiple seller accounts and marketplaces. * Fetching and synchronizing advertising campaign data. * Retrieving campaign, ad group, keyword, targeting and search-term data. * Accessing advertising performance reports and historical data. * Building an intelligent campaign creation workflow. * Allowing users to select products and define advertising objectives. * Generating campaign structures, keyword suggestions, match types, targeting strategies, initial bids and budgets. * Allowing users to review, edit and approve campaigns before launch. * Creating campaigns through the Amazon Ads API. * Verifying successful campaign creation and handling API errors. The initial product should prioritize Sponsored Products campaigns, with architecture that can support additional advertising formats in the future. 2. Data Engineering & Infrastructure We need a robust data infrastructure capable of processing, organizing and analysing large volumes of advertising and business data. Responsibilities include: * Designing scalable database and data warehouse architecture. * Developing automated ETL/ELT pipelines. * Integrating Amazon advertising and relevant e-commerce data sources. * Managing historical campaign, keyword, sales and performance data. * Building reliable background synchronization processes. * Developing data validation, transformation and processing systems. * Managing API rate limits, retries and failed synchronization. * Designing secure multi-tenant data architecture. * Ensuring data accuracy, availability, performance and scalability. * Building the foundation for analytics, reporting and AI intelligence. Experience with Python, SQL, PostgreSQL, AWS, BigQuery, ClickHouse, Redis and distributed data processing is valuable. 3. AI Intelligence & Recommendation Engine We want to develop a genuinely intelligent platform rather than a conventional PPC dashboard. The software should analyse advertising performance, product information and business objectives to provide useful recommendations and automate workflows. Responsibilities include: * Developing AI-powered advertising analysis. * Integrating LLMs such as OpenAI and Claude where appropriate. * Building intelligent AI agents that understand campaign and business performance. * Generating actionable advertising recommendations. * Analysing keyword performance, wasted spend and growth opportunities. * Identifying underperforming campaigns and potential improvements. * Providing intelligent explanations behind recommendations. * Building automated reporting and business insights. * Connecting AI agents with the data infrastructure and optimization systems. * Developing approval-based and automated execution workflows. The system should be able to explain why it recommends an action, what data supports that decision and what potential impact it may have. We are not looking for someone who simply connects an LLM API and calls it an AI product. We want practical, reliable AI intelligence built around real advertising data. 4. PPC Automation & Campaign Management A major objective is to reduce the amount of manual work required to manage Amazon advertising. The platform should progressively support: * Automated campaign creation and management. * Keyword organization and targeting. * Bid and budget recommendations. * Search-term harvesting. * Negative keyword identification and management. * Detection of wasted advertising spend. * Campaign performance monitoring. * Budget utilization monitoring. * Identification of scaling opportunities. * Automated bid and budget adjustments. * Notifications and alerts for important campaign events. * Performance reporting and optimization history. We want three levels of automation: 1. Assisted mode – the software provides recommendations. 2. Approval mode – the software proposes changes and the seller approves execution. 3. Autopilot mode – the software executes approved optimization rules within predefined limits. All automated actions must have appropriate safeguards, spending limits, audit logs, error handling and the ability to pause automation. 5. Profitability & Business Intelligence We want the platform to move beyond basic ACOS and ROAS reporting. It should eventually incorporate business-level information such as: * Product selling price. * Cost of goods sold. * Amazon fees. * Contribution margins. * Advertising expenditure. * Sales and conversion performance. * Break-even ACOS. * Return on advertising spend. * Product profitability. The objective is to help sellers make advertising decisions based on actual business profitability rather than advertising metrics alone. 6. SaaS Application & User Experience Our development team must deliver a complete, production-ready SaaS application. This includes: * Modern and intuitive frontend. * Secure backend architecture. * User registration and authentication. * Subscription and billing integration. * Multi-tenant account management. * Seller dashboards and reporting. * Campaign management interfaces. * AI recommendation and execution centre. * User permissions and account settings. * Notifications and activity logs. * Responsive application design. * Secure storage and management of sensitive API credentials. The platform must be designed to support multiple customers and scale as the business grows. 7. Testing, Deployment & Production We expect the selected team to take responsibility for the complete production process. This includes: * Technical architecture and development planning. * Development environment and cloud infrastructure. * API integration testing. * Data pipeline testing. * AI and model validation. * Application security. * Performance and scalability testing. * Error monitoring and logging. * Production deployment. * Documentation and knowledge transfer. * Post-launch bug fixes and technical support. We want a properly tested and functioning commercial SaaS product, not just a prototype or a collection of disconnected modules. Required Experience Applicants must demonstrate extensive practical experience in: * Building and launching complete SaaS platforms. * Amazon Ads API and Amazon SP-API integrations. * Data engineering, ETL pipelines and data warehouses. * Backend and frontend development. * AI engineering and LLM integrations. * Production AI agents and automated workflows. * Cloud infrastructure and scalable architecture. * E-commerce advertising technology. * Building secure multi-tenant applications. * Deploying and maintaining production software. Please provide examples of actual products or systems you have built and deployed. We are particularly interested in projects involving advertising technology, e-commerce analytics, PPC automation, AI-driven SaaS or similar platforms. Initial Project Engagement We prefer to begin with a detailed technical discovery and architecture phase. The selected candidate or team will be expected to: 1. Review the complete product vision and requirements. 2. Propose the end-to-end technical architecture. 3. Identify the required technologies, infrastructure and API dependencies. 4. Provide a complete development roadmap. 5. Break the project into clear milestones and deliverables. 6. Estimate realistic development costs and timelines. 7. Demonstrate relevant technical capabilities through an agreed initial prototype. We intend to move forward with milestone-based development and expect transparent communication, regular progress updates and clear deliverables. Application Questions Please answer the following: 1. Have you personally built and launched a complete SaaS platform from concept to production? Please share examples. 2. What experience do you have with Amazon Ads API and Seller Partner API? Describe your actual integrations. 3. Have you developed a platform that automatically creates and manages advertising campaigns? 4. What data engineering architecture would you recommend for this project? 5. What experience do you have developing AI agents and integrating LLMs into commercial software? 6. Have you built production systems that analyse large datasets and automatically generate or execute business recommendations? 7. What is the size and technical composition of your team? Who will personally handle architecture, backend, data engineering and AI development? 8. Can you take responsibility for the complete end-to-end production of this platform? 9. Please provide relevant portfolio examples, technical demonstrations and your estimated budget and timeline.