AI-Augmented Full-Cycle Product Engineer

WHAT YOU'LL DO: Discovery & requirements - Engage business stakeholders directly to elicit, clarify, and document business requirements. - Turn ambiguous problems into clear, testable requirements — using AI to accelerate research, drafting, and gap analysis. Solution design & specification - Produce technical solutions and specifications: data models, API contracts (OpenAPI), service boundaries, sequence and architecture diagrams. - Make sound architecture decisions aligned with our microservices, event-driven, and security-first principles, and document the trade-offs. Build (backend / frontend / mobile) - Implement backend services in Python and/or Golang on PostgreSQL, with appropriate messaging (Kafka) and caching. - Build web frontends (Vue/Nuxt or React) and, where needed, mobile features (Android/Kotlin, iOS/Swift). - Write reusable, testable, efficient code, using AI coding agents to move fast while keeping the codebase clean and reviewable. Test & quality - Design and execute meaningful test coverage — unit, integration, API, and end-to-end — leveraging AI-assisted test generation and review. - Own quality end to end: you are the developer *and* the tester, and "done" means verified. Ship & operate - Deliver through our CI/CD pipelines and infrastructure as code. - Build observability in at design time (metrics, logs, tracing) and support what you ship. HOW WE WORK - Stack : AWS, Kubernetes, Python & Golang, PostgreSQL, Kafka, Vue/Nuxt & React, Android/iOS, Terraform/Ansible, GitLab CI/CD. - Principles : clear and maintainable architecture, security-first, scalability and reliability, high observability and automation, cloud-native and microservices-oriented design, and a strong focus on performance, resilience, and operational excellence. - AI-native by default : we expect AI to be part of how you think, design, build, and test — used deliberately and responsibly. WHAT SUCCESS LOOKS LIKE: Within your first few months, you independently take a real business need through the full cycle — requirements, spec, implementation across the relevant layers, tests, and a production release with observability — at a speed and quality that would traditionally have required a small team, and you help the rest of engineering raise its AI fluency along the way.