Responsibilities:

• Develop and maintain high-performance RESTful APIs with Node.js.
• Ensure scalability and resilience by applying caching and optimizing queries in PostgreSQL.
• Containerize and standardize services with Docker and Docker Compose.
• Operate GCP services (Cloud Run, Cloud Functions, Cloud Storage) for provisioning, deployment, and maintenance.
• Implement tests (unit, integration, and E2E) ensuring quality and stability.
• Participate in code reviews and continuous improvement of CI/CD processes.
• Collaborate with product and other engineering teams.
• Ensure quality, performance, and best practices.

Requirements:

• Proven experience with Node.js in the development of services and RESTful APIs.
• Knowledge in Python for the development and maintenance of small services.
• Mastery of Postgres databases, with a focus on performance.
• Mastery in administration, architecture, and security of services on Google Cloud Platform (GCP), with practical experience in Cloud Run, Cloud SQL, especially PostgreSQL, and advanced network configurations, including VPCs, Cloud NAT, Load Balancers, and Firewalls.
• Advanced experience in containerization, with creation, optimization, and management of Docker images to ensure fast, efficient, and consistent deployments.
• Experience with container orchestration using Kubernetes (preferably GKE).
• Ability to provision, automate, and version infrastructure as code (IaC), using tools like Terraform.
• Experience in building and maintaining continuous integration and delivery pipelines (e.g., GitHub Actions, GitLab CI, Cloud Build) focused on the automated deployment of APIs (such as Node.js) and frontend applications.
• Configuration of logs, performance metrics, and alerts using the Google Cloud Operations Suite (Cloud Logging, Cloud Monitoring) to ensure the health and high availability of applications.
• Mastery in access and cost management, with security policy configuration via IAM, as well as monitoring and optimization of computational and database costs.
• Mastery of the model lifecycle in Vertex AI, including training, versioning in the Model Registry, deployment on scalable endpoints, and use of Vertex AI Studio.
• Practice in automated testing and participation in code reviews/CI-CD.

Nice to have:

• Complete higher education in Computer Science, Software Engineering, Information Systems, or related areas.
• Microservices architecture and messaging (Pub/Sub, Kafka).
• Monitoring and observability with Prometheus and Grafana.