DevOps Engineer (GCP)
ITEAMLY SPÓŁKA Z OGRANICZONĄ ODPOWIEDZIALNOŚCIĄ
⚲ Warszawa
23 000–35 000 zł netto (+ VAT) / mies.
Wymagania
- Google Cloud Platform
- Terraform
- Python
Opis stanowiska
Nasze wymagania:
6+ years in DevOps / platform / SRE / security engineering or backend
Production experience with GCP (or comparable cloud + willingness to move to GCP)
Terraform / IaC — writing modules from scratch, not just maintaining existing ones
CI/CD for containerized services
Security: IAM, secrets management, tenant isolation, incident response
Python — backend code, not just scripts (FastAPI)
Mile widziane:
Neo4j or vector databases
Familiarity with AI/LLM workloads (OpenAI, Gemini, Anthropic)
Experience with async jobs and traceable AI workflows
O projekcie:
• Observability stack: logs, metrics, traces, dashboards, SLOs
• Comfort working in a startup — designing, implementing, and documenting solutions independently
• GitHub Actions and multi-service deployment pipelines
Zakres obowiązków:
Own GCP infrastructure across dev, staging, and production: Cloud Run, Firebase, Firestore, Storage, Secret Manager, Artifact Registry, Cloud SQL
Manage Terraform and IaC hygiene
CI/CD reliability — rollback paths, environment parity, deployment safety
IAM, secrets, service-to-service auth, logging, audit trails, data retention
Observability: logs, metrics, latency, alerts, runbooks, SLOs
Security review for document ingestion, file storage, and partner-facing APIs
Backend implementation where security and operations meet product behavior
Oferujemy:
Fully remote work,
Opportunity to work on modern ML/AI infrastructure,
Flexible cooperation model,
Exposure to cutting-edge technologies,
Collaborative and supportive team environment.
6+ years in DevOps / platform / SRE / security engineering or backend
Production experience with GCP (or comparable cloud + willingness to move to GCP)
Terraform / IaC — writing modules from scratch, not just maintaining existing ones
CI/CD for containerized services
Security: IAM, secrets management, tenant isolation, incident response
Python — backend code, not just scripts (FastAPI)
Mile widziane:
Neo4j or vector databases
Familiarity with AI/LLM workloads (OpenAI, Gemini, Anthropic)
Experience with async jobs and traceable AI workflows
O projekcie:
• Observability stack: logs, metrics, traces, dashboards, SLOs
• Comfort working in a startup — designing, implementing, and documenting solutions independently
• GitHub Actions and multi-service deployment pipelines
Zakres obowiązków:
Own GCP infrastructure across dev, staging, and production: Cloud Run, Firebase, Firestore, Storage, Secret Manager, Artifact Registry, Cloud SQL
Manage Terraform and IaC hygiene
CI/CD reliability — rollback paths, environment parity, deployment safety
IAM, secrets, service-to-service auth, logging, audit trails, data retention
Observability: logs, metrics, latency, alerts, runbooks, SLOs
Security review for document ingestion, file storage, and partner-facing APIs
Backend implementation where security and operations meet product behavior
Oferujemy:
Fully remote work,
Opportunity to work on modern ML/AI infrastructure,
Flexible cooperation model,
Exposure to cutting-edge technologies,
Collaborative and supportive team environment.
🔍 Dekoder Ogłoszenia
🔴
writing modules from scratch, not just maintaining existing ones
Oczekuje się od Ciebie tworzenia nowych, złożonych modułów Terraform, a nie tylko drobnych modyfikacji istniejących.
🔴
Comfort working in a startup — designing, implementing, and documenting solutions independently
Będziesz musiał samodzielnie podejmować decyzje projektowe i implementacyjne, często bez szczegółowych wytycznych.
🔴
Own GCP infrastructure across dev, staging, and production
Będziesz w pełni odpowiedzialny za całą infrastrukturę GCP, co może oznaczać pracę w trybie 24/7 w przypadku awarii.
🔴
Python — backend code, not just scripts (FastAPI)
Oczekuje się od Ciebie tworzenia pełnoprawnych aplikacji backendowych w Pythonie, a nie tylko prostych skryptów automatyzujących.
🟡
Opportunity to work on modern ML/AI infrastructure
Projekt może być na wczesnym etapie rozwoju, a infrastruktura ML/AI może być jeszcze w fazie budowy lub eksperymentów.