Applied AI Engineer

DoiT International

Applied AI Engineer at DoiT building and integrating innovative solutions in cloud technology. Focusing on end-to-end software delivery using modern tech stack.

Posted 7/15/2026full-timeRemote • 🇮🇩 IndonesiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in end-to-end product delivery, proficient in backend and frontend engineering with a strong focus on Python, Go, TypeScript, and cloud infrastructure on GCP. Capable of integrating third-party APIs and utilizing AI tooling effectively throughout the software development lifecycle.

Highest-signal resume keywords

End-To-End Product DeliveryBackend Engineering (Python/Go)Frontend Engineering (TypeScript/React)Cloud Infrastructure (GCP)API and Systems Integration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonGoTypeScriptReactCloud RunGCPREST APIsOAuthEvent-Driven PatternsSecrets Management

Tools & Technologies

Claude CodePub/SubSOPSSecret Manager

Industry Keywords

Software EngineeringProduct DeliveryWorkflow AutomationBusiness Process OrchestrationAppSec Hygiene

Tech Stack

Tools & technologies

CloudGoGoogle Cloud PlatformPythonReactSDLCTypeScript

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

Typically 3-9 years of professional software engineering experience
End-to-end product delivery: you have shipped and operated production software with real users - not just features in isolation, but complete products from brief to deployment.
Backend engineering: proficient in Python or Go (or both); you can own a service from design to Cloud Run deployment.
Frontend engineering: proficient in TypeScript / React; comfortable building UI-complete features such as internal dashboards or admin tools.
Cloud infrastructure: you deploy and operate on GCP and understand IAM, secrets management, Cloud Run, and deployment pipelines.
API and systems integration: you have connected multiple third-party platforms in production - REST APIs, webhooks, OAuth flows, event-driven patterns - and know how to handle failures, retries, and schema drift.
Security baseline: no hardcoded credentials; you use secrets management (SOPS, Secret Manager, or equivalent); no PII in logs; solid AppSec hygiene.
AI in the development lifecycle: you use AI tooling (Claude Code or equivalent) actively across the full SDLC, review AI-generated output critically for correctness, security, and type safety, and can direct AI on multi-step implementation tasks while owning the result.

Benefits

Comp & perks

Unlimited PTO
Flexible Working Options
Health Insurance
Parental Leave
Employee Stock Option Plan
Home Office Allowance
Professional Development Stipend
Peer Recognition Program