ABOUT DAWERONE DawerOne is an AI-native venture office and consulting practice that helps growing companies build, modernize, and scale technology products without unnecessary process or complexity. ROLE OVERVIEW This is a contract position to start, with the opportunity to increase scope over the first few months and transition to a full-time position if it is a strong fit for both sides. We are seeking a Senior Backend Engineer to work on a client product, building and maintaining its backend with a strong focus on Python, AWS Lambda, APIs, and integrations with external AI services. You will be responsible for building production backend services, integrating third-party AI APIs, handling failures and edge cases, and ensuring the system is reliable, observable, and cost-effective. KEY RESPONSIBILITIES BACKEND DEVELOPMENT • Build and maintain production backend services using Python. • Develop and maintain APIs that support the application’s core functionality. • Build and maintain AWS Lambda services and supporting AWS infrastructure. • Design clean, maintainable backend components and integrations. • Implement business logic, data processing, and service-to-service communication. • Debug and resolve backend issues across development and production environments. • Improve backend performance, reliability, and maintainability as the product evolves. AI AND THIRD-PARTY INTEGRATION • Integrate external AI services for transcription, language models, and scoring. • Build reliable API clients and service integrations rather than tightly coupling vendor-specific logic throughout the application. • Handle API authentication, request/response validation, timeouts, retries, rate limits, and failures. • Account for unexpected or malformed responses from external providers. • Design appropriate fallback and failure-handling behavior when an external service is unavailable. • Monitor AI API usage and help control token consumption and vendor costs. • Keep integrations maintainable as external vendors change their APIs and capabilities. • Understand the practical differences between deterministic application logic and nondeterministic AI-powered services. INFRASTRUCTURE AND ENVIRONMENTS • Stand up a staging environment and a real deployment pipeline where none exists. • Establish IAM fundamentals: named users, least privilege, no root usage. • Build environment configuration and secret management that holds up across development, staging, and production. RELIABILITY & OBSERVABILITY • Build backend services that remain stable when external dependencies are slow or unavailable. • Implement appropriate timeouts, retries, error handling, and graceful failure behavior. • Add useful logging, metrics, and monitoring to backend services and AI integrations. • Investigate production failures and identify whether the root cause is in our application, infrastructure, authentication, test data, or an external dependency. • Identify reliability issues before they become recurring production problems. • Balance reliability and engineering effort without over-engineering a relatively straightforward system. TESTING & ENGINEERING QUALITY • Write automated unit, integration, and API tests for backend functionality. • Test both successful and failure scenarios, particularly around external service integrations. • Validate API responses, error conditions, authentication, data handling, and business logic. • Build reusable test utilities and fixtures where appropriate. • Use automated testing as part of normal backend development rather than treating testing as a separate QA function. • Integrate tests into the development and CI/CD workflow. • Use AI coding tools responsibly to accelerate development, debugging, documentation, and test creation while reviewing generated code before it reaches production. API & INTEGRATION ENGINEERING • Design and maintain REST APIs and service interfaces. • Work with third-party APIs and SDKs. • Handle authentication mechanisms such as API keys, OAuth, and service credentials where required. • Validate external inputs and protect the application from unexpected or invalid data. • Design integrations that are observable and easy to troubleshoot. • Ensure data is passed correctly between backend services and external platforms. DELIVERY AND COLLABORATION • Read an undocumented system and reconstruct what it is actually doing, as the first real task on it. • Work alongside a dedicated QA engineer to define what a safe release requires on the backend. • Report plainly on what is fragile, what is fixed, and what a proposed change will cost to build. REQUIRED QUALIFICATIONS • 5+ years of professional backend software engineering experience. • Strong experience building production applications with Python. • Hands-on experience with AWS Lambda and serverless backend development. • Strong experience designing and consuming REST APIs. • Experience integrating third-party APIs and external services. • Experience working with LLM APIs or other AI-powered APIs. • Strong understanding of asynchronous operations, error handling, retries, timeouts, and rate limiting. • Experience writing automated tests for backend systems. • Experience with Git and modern software development workflows. • Experience debugging production backend applications. • Understanding of logging, monitoring, and basic observability practices. • Ability to work independently and take ownership of backend development. • Strong communication skills and the ability to make practical technical decisions without excessive process. • At least two hours of daily overlap with US Eastern time. PREFERRED QUALIFICATIONS • Experience with OpenAI, Anthropic, Google, AWS Bedrock, or comparable AI APIs. • Experience building systems that depend on multiple external APIs. • Experience with AWS services beyond Lambda, such as API Gateway, SQS, EventBridge, DynamoDB, S3, or CloudWatch. • Experience with PostgreSQL or another relational database. • Experience with asynchronous or event-driven backend architectures. • Experience managing AI API costs, token usage, rate limits, and vendor quotas. • Experience with CI/CD and automated deployment pipelines. • Experience using AI coding tools such as GitHub Copilot, Claude Code, Cursor, or similar tools. • Experience building reliable systems around nondeterministic AI outputs. • Experience working in a small engineering team where engineers have significant ownership. WHAT THIS ROLE IS NOT If you are looking for applied ML, model training, or evaluation research, this is not that role. Measuring model accuracy, benchmarking vendors against each other, and systematic prompt testing against a scored dataset is a separate function we bring in once there is something specific to measure. This role builds the plumbing that makes that possible later. It does not do the research itself. WHAT STRONG CANDIDATES WILL DEMONSTRATE Strong candidates should be prepared to walk through: • A Python backend system they personally built and maintained. • How they have integrated an external API or AI service into a production application. • How they handle an external vendor that becomes slow or unavailable. • How they approach retries, timeouts, rate limits, and unexpected API responses. • How they monitor and control API or AI-related costs. • How they structure third-party integrations so they remain maintainable. • How they test backend functionality and external integrations. • How they investigate a production issue when the application, infrastructure, and external vendor could all be potential causes. • How they use AI coding tools while maintaining engineering quality and ownership. • How they decide when a solution should remain simple versus when additional infrastructure or architecture is justified. MEASURES OF SUCCESS Within the first month, the engineer will be expected to: • Become the primary owner of the Python backend and Lambda services. • Deliver backend features reliably and with appropriate automated coverage. • Establish robust integrations with our external AI providers. • Improve handling of vendor failures, timeouts, rate limits, and unexpected responses. • Establish useful logging and monitoring around backend and AI integrations. • Maintain visibility into AI API usage and costs. • Reduce recurring backend and integration issues. • Improve the reliability and maintainability of the backend without unnecessary complexity. • Become a dependable technical owner for backend development and production issues.
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