Applied AI Engineer -Croatia(Remote)

DoiT is a global technology company specializing in cloud-driven business growth and innovation. The role is within the Business Systems Engineering division, part of the Fusion platform engineering team that builds scalable event-driven platforms integrating multiple SaaS systems on GCP.

• 3-9 years professional software engineering experience.
• Experience shipping and operating production software end to end.
• Proficient backend engineering in Python and/or Go with deployment on Cloud Run.
• Proficient frontend engineering in TypeScript and React for dashboards and admin tools.
• Experience with GCP infrastructure, IAM, secrets management, Cloud Run, and deployment pipelines.
• Experience integrating multiple third-party platforms with REST APIs, webhooks, OAuth, and event-driven patterns including failure handling.
• Strong security practices: no hardcoded credentials, secrets management (e.g., SOPS, Secret Manager), no PII in logs, strong AppSec hygiene.
• Active use of AI tooling (Claude Code or equivalent) for specification, coding, review, and directing AI on tasks.

Nice to have:

• Experience with workflow automation platforms like n8n, Zapier.
• Experience with BPMN/workflow orchestration engines such as Camunda, Temporal.
• Systems-level programming including async runtimes and IPC.
• Knowledge of graph algorithms or graph databases such as Neo4j and Cypher.
• Experience with Rust and Tauri for desktop apps.
• Infrastructure as code with Terraform.
• Experience with LLM API integration, prompt design, and AI safety and compliance considerations.

• Own internal tools end to end from problem understanding through deployment and iteration.
• Build services using Python/Go on Cloud Run, and internal web apps with TypeScript/React.
• Integrate with third-party SaaS platforms and cloud APIs using REST, webhooks, OAuth, and event-driven patterns.
• Build and maintain workflow automation and business process orchestration as observable BPMN flows.
• Use AI tooling, specifically Claude Code, throughout the development lifecycle.
• Review AI-generated code for correctness, security, and type safety.
• Operate production systems including monitoring, reliability, and failure handling.
• Contribute to shared engineering standards, playbooks, and internal tooling.