AI Native Full Stack Engineer

Description

About Gapstars

About Gapstars
At Gapstars, we partner with some of Europe's most ambitious companies, from disruptive tech startups to established Benelux accounting firms, helping them build high-performing teams in engineering and finance.

Headquartered in the Netherlands, with talent hubs in Sri Lanka and Portugal, we are home to 300+ professionals who thrive on solving real-world challenges with modern solutions. Our teams work across domains, from networking, marketplaces, SaaS, and AI to embedded finance and accounting, delivering scalable solutions that drive meaningful outcomes.

The Role
We are looking for an experienced full stack engineer to take independent ownership of complex features across Gapstars products. You will strengthen its web applications, backend services and cloud operations, and help deliver a mobile application planned for release in 2027.

This is a hands-on senior engineering role requiring strong production GCP experience, practical Kubernetes knowledge, DevOps exposure and mobile development experience. Personal ownership of at least one successful Apple App Store release is essential. You will use AI-assisted development throughout your workflow while retaining full responsibility for architecture, code quality, security, testing and production outcomes.

The Role

AI Native Full Stack Engineer

Requirements

Key Responsibilities
Full stack development and engineering ownership
  • 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.
  • Turn incomplete requirements into practical technical options, manageable delivery increments and testable acceptance criteria.
  • 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.

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