AI Native Full Stack Engineer

Own work from problem clarification and technical design through implementation, automated testing, deployment and production follow-up.

Build and maintain applications using TypeScript, React, Next.js and Node.js, with secure APIs and third-party integrations.

Understand and safely extend an existing codebase, diagnosing unfamiliar behaviour and preventing regressions.

Design PostgreSQL schemas, transactions, migrations and queries with attention to integrity, compatibility, performance and recovery; work with an ORM such as Prisma.

Review code, document important decisions, support other engineers and communicate trade-offs clearly. GCP, Kubernetes and DevOps

Deploy, operate and troubleshoot production workloads on GCP, including services relevant to COSMOS such as Cloud Run, Cloud SQL, Cloud Build, IAM, networking, secrets management, logging and monitoring.

Work confidently with Docker and Kubernetes deployments, services, configuration, health checks, resource management and troubleshooting.

Maintain CI/CD pipelines, separate development and production environments, and support safe releases, validation and rollback.

Improve observability, investigate incidents and address root causes, balancing reliability, security and cost.

Apply appropriate deployment architecture to the workload and explain when managed services or Kubernetes are justified Mobile development and release ownership

Contribute to the architecture and implementation of the COSMOS mobile application using a production mobile framework.

Develop secure mobile authentication, API integration, local storage and device-specific behavior, with attention to stability and performance.

Own release activities covering build configuration, signing, device testing, beta distribution, store submission, review feedback and subsequent updates.

Coordinate with product and QA to prepare the application for release and monitor issues after launch. AI-native engineering

Use AI coding assistants and agents regularly for implementation, debugging, refactoring, testing and documentation.

Direct AI tools with clear context, constraints and manageable tasks. Independently review, explain and validate generated code; identify and correct misleading, insecure or unsuitable output.

Demonstrate where AI improved delivery and where engineering judgment was needed to correct or reject its output.

Protect company code, credentials and sensitive information when using AI tools. Quality, communication and delivery.

Own the quality of delivered work through appropriate unit, integration and end-to-end tests, working closely with QA.

Communicate estimates, assumptions, dependencies and uncertainty clearly; raise risks early with practical alternatives.

Validate complete user journeys and release readiness, including error handling and access controls.

Maintain useful technical documentation and proactively communicate progress, blockers and production issues.