Full Stack Web Developer

About the Role

As a full stack web developer, you'll build full-stack solutions, ship features, and solve real engineering problems across client projects and our internal tools. AI coding tools are the default way we work here, not an optional extra. That means less time on boilerplate and more time on the parts AI still can't do reliably on its own: understanding what's being asked, catching edge cases, reasoning through unclear requirements, and making sure what ships works.

You will work closely with clients, join their calls, and identify their business pain points directly rather than waiting for a brief to land in your queue. You then bring that context back to our internal tech team to develop solutions.

You'll still write code directly, especially where judgment and precision matter. This role suits someone who already pairs with tools like Claude Code or Cursor in production and wants to keep sharpening that skill on top of solid engineering fundamentals. It is not a fit for someone who only types out specs by hand.

This is a remote position. However, during deployment periods, the candidate will be required to travel to Clickr's office in Singapore.

Responsibilities

• Design and build solutions for new projects, ongoing builds, maintenance work, and internal tools.
• Implement full-stack features across frontend, backend, APIs, CMS platforms, integrations, and cloud-hosted applications.
• Use AI coding tools as your default workflow for scaffolding, implementation, refactoring, debugging, documentation, and test generation.
• Take AI-accelerated work to production quality. AI may get you most of the way there fast, but you own the edge cases and business rules.
• Catch problems in AI-generated code: logic errors, weak error handling, security gaps, missing edge cases, over-engineered solutions.
• Identify and fix issues in business logic, API integrations, performance, accessibility, maintainability, and security before they become problems.
• Work directly with clients to understand their business pain points firsthand: sit in discovery calls, ask the questions a spec won't answer, and see how their teams actually work day to day.
• Translate what you learn from clients into technical scope, and translate technical constraints back into language clients can act on.
• Collaborate closely with our internal tech team so client context, decisions, and constraints don't get lost between the client's side and ours.
• Demo work-in-progress to clients, gather feedback early, and adjust before problems get expensive.
• Help build and maintain the team's AI coding workflows: prompt patterns, tool configurations, reusable instructions, and small automations.
• Troubleshoot staging and production issues across code, hosting, databases, APIs, logs, and configuration.
• Communicate progress, blockers, risks, and changes clearly on the platforms the team already uses.
• Help build and maintain the team's AI coding workflows: prompt patterns, tool configurations, reusable instructions, and small automations.
• Troubleshoot staging and production issues across code, hosting, databases, APIs, logs, and configuration.
• Communicate progress, blockers, risks, and changes clearly on the platforms the team already uses.

Experience Needed

• 2+ years of professional full-stack development. Years matter less than proven ability to work well with AI coding tools and ship production-quality work. Fresh graduates are also encouraged to apply.
• Experience in a client-facing role: consulting, agency delivery, solutions or implementation engineering, or similar. You should be comfortable running a conversation with a non-technical stakeholder.
• Working experience with modern web frameworks such as React, Node.js, and Next.js. Python is a plus.
• Degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience. Bootcamp plus a solid portfolio or production work is fine.
• Some exposure to CMS or eCommerce platforms such as WordPress, Drupal, or Magento. It doesn't need to be deep, but you should understand the general architecture.
• Experience integrating systems with REST APIs. GraphQL and webhooks are a plus if you haven't used them hands-on yet.
• Familiarity with cloud hosting platforms such as AWS, Kinsta, or DigitalOcean.
• Experience with Git in a team setting and a standard dev workflow: scope, build, review, deploy.
• Daily, hands-on production experience with AI coding tools such as Claude Code or Cursor. This is the most important requirement. You should be able to talk concretely about how you use these tools, where you trust them, and where you double-check them.
• Reasonable debugging ability across frontend, backend, and at least one other layer, whether that's database, infra, or a third-party integration.
• A working understanding of the fundamentals: HTTP, databases, authentication, API design, error handling, testing, deployment. You don't need mastery of all of them, but you should be able to reason about them rather than recite them.
• Exposure to CI/CD, Linux, environment variables and secrets, and staging vs production environments is a plus, not a hard requirement.
• Experience with workflow automation tools like n8n, Zapier, or Make is a bonus.

What Will Help You Succeed

• Good written and spoken communication. You'll work with people across countries and time zones.
• Strong research habits: documentation, source code, logs, tests, search, and AI tools, used to understand unfamiliar problems and verify solutions.
• The discipline to manage your own time and output without close supervision.
• Fundamentals strong enough to tell when AI is right, when it's confidently wrong, and when it's guessing.
• Good habits for juggling multiple projects at once.
• The instinct to ask good questions before building the wrong thing.
• Care for the user, the client, and the business problem behind the technical request.
• The ability to earn a client's trust quickly and hold it when things go wrong, since you're often the technical face they see.
• Comfort moving between code, logs, documentation, meetings, and production issues.
• Practical judgment on when to build properly, when to simplify, and when to flag risk.
• A habit of leaving code, docs, workflows, and processes better than you found them.