Applied AI Engineer

D
DoiTCloud Technology
This role is based remotely in Eastern Europe or Indonesia.Full-TimeMiddle
Salary not disclosed
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Job Details

Experience
Typically 3-9 years of professional software engineering experience.
Required Skills
PythonGCPOAuthTypeScriptGoReactRESTful APIs

Requirements

  • 3-9 years of professional software engineering experience.
  • End-to-end product delivery: experience shipping and operating production software with real users.
  • Backend engineering: proficient in Python or Go.
  • Frontend engineering: proficient in TypeScript / React.
  • Cloud infrastructure: experience deploying and operating on GCP including IAM, secrets management, and Cloud Run.
  • API and systems integration: experience connecting multiple third-party platforms using REST APIs, webhooks, and OAuth.
  • Security baseline: experience with secrets management and solid AppSec hygiene.
  • AI in the development lifecycle: active use of AI tooling (Claude Code or equivalent) and ability to critically review AI-generated output.

Responsibilities

  • Own internal tools end to end - from understanding the operational problem through design, implementation, deployment, and iteration.
  • Build across the stack as the product requires: Python/Go services on Cloud Run, TypeScript web apps and internal admin tools, event-driven integrations on Pub/Sub, and cloud infrastructure on GCP.
  • Integrate with third-party SaaS platforms and cloud APIs - handling REST, webhooks, OAuth, and event-driven patterns, including failures, retries, and schema drift.
  • Build and maintain workflow automation and business process orchestration as named, observable flows rather than logic buried in vendor scripts.
  • Use AI tooling effectively across the development lifecycle - spec, code, and review - directing Claude as a coding collaborator while owning the output.
  • Review AI-generated code before it ships for correctness, security, and type safety.
  • Operate what you ship: monitoring, reliability, cost, and failure modes in production.
  • Contribute to shared engineering standards, playbooks, and internal tooling.
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