AI Augmented Full-Cycle 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.