Applied AI Engineer

About this role

Location As an AI Applied Engineer, you will be part of the Business Systems Engineering (BSE) division at DoiT - the division that builds the platform the rest of the company runs on. You'll join Fusion, our platform engineering team. This role is based remotely in Eastern Europe or Indonesia.

Who We Are DoiT is a global technology company that works with cloud-driven organizations to leverage the cloud to drive business growth and innovation. We combine data, technology, and human expertise to ensure our customers are operating in a well-architected and scalable state - from planning to production. Delivering DoiT Cloud Intelligence, the only solution that integrates advanced technology with human intelligence, we help our customers solve complex multi-cloud problems and drive efficiency. With decades of multicloud experience, we have specializations in Kubernetes, GenAI, CloudOps, and more. An award-winning strategic partner of AWS, Google Cloud, Microsoft Azure and Ingram Micro, we work alongside more than 4,000 customers worldwide.

The Opportunity DoiT runs on dozens of SaaS platforms spanning CRM, ERP, support, marketing, and billing - and the list keeps growing. Connecting them one integration at a time doesn't scale: point-to-point links multiply as the square of the systems, business logic gets trapped inside vendor scripts, and critical processes go dark the moment they break. Fusion is building the alternative. That alternative - internally, Project Babylon - is a composable, event-driven platform: Packaged Business Capabilities that expose each domain (Customer, Billing, Support) as a clean, versioned API; a process orchestrator that runs business workflows as named, observable BPMN flows; and a canonical event log underneath. It turns a sprawl of vendor systems into a coherent foundation the whole company builds on. And because every capability is a stable, semantic API with narrow permissions and a full audit trail, AI agents become first-class consumers of the platform - not bolted-on scripts. This is a high-ownership engineering role for builders who want real distributed-systems problems - event-driven architecture, reconciliation, idempotency, observability - and want to see their work land across the business within weeks. You'll ship end to end across a modern stack - Python and Go services on GCP Cloud Run, TypeScript/React surfaces, Pub/Sub, and BPMN orchestration - from shaping the brief through production operation. AI is core to how we work in two directions: we use Claude Code across the lifecycle (spec, build, review) so your time goes to design and judgment rather than boilerplate, and we build the platform so that agents can operate it safely. Engineering quality, correctness, and ownership stay firmly with you. The work is concrete and varied - reconciling marketing leads into the CRM, giving Finance real-time forecasting instead of spreadsheet exports, turning a quarterly access audit into an observable flow that produces its own evidence. You'll have real room to shape how DoiT builds and operates its internal systems as we scale. 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.

Qualifications Typically 3-9 years of professional software engineering experience. Experience is a signal, not a gate - we care more about what you've shipped and how you think.

Must have :

• 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.

Nice to have:

• Workflow automation platforms (n8n, Zapier, or similar).

• Business process orchestration (Camunda, Temporal, or similar BPMN/workflow engines).

• Systems-level programming: async runtimes, IPC, process lifecycle, protocol clients.

• Graph algorithms (cycle detection, topological sort) or graph databases (Neo4j, Cypher).

• Rust / Tauri for desktop applicat

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