Cloud Engineer

3 semanas atrás


Porto, Porto, Portugal DIGESTAID - Digestive Artificial Intelligence Development Tempo inteiro

Mission

Join the engineering team to support our
cloud migration roadmap
to a modern, scalable, and compliant ML infrastructure on Google Cloud Platform (GCP).

You will learn how to operate production-grade ML systems under the mentorship of a Lead MLOps Engineer, while contributing directly to infrastructure modernization, CI/CD pipelines, and MLOps workflows.

Responsibilities


• Support our
cloud migration roadmap
to modern GCP architecture.


• Implement
security & performance
improvements: Cloud Armor, CDN, KMS encryption, IAM policies.


• Build
observability infrastructure
: Prometheus, Grafana dashboards, distributed tracing, SLO monitoring.


• Deploy
containerized workloads
to Cloud Run and GKE Autopilot with autoscaling and GPU support.


• Set up
async messaging
with Pub/Sub queues, idempotency, and dead-letter queues.


• Build
Infrastructure as Code
with Terraform/Terraspace and CI/CD pipelines (GitHub Actions).


• Optimize
storage & costs
: lifecycle policies, tiered storage, resource labeling, budget monitoring.


• Support
compliance initiatives
: audit logs, retention policies, least-privilege access (FDA/HIPAA).


• Write
clear documentation
and operational runbooks for all infrastructure components.

Stack You'll Work With



Cloud:
GCP (Cloud Run, GKE Autopilot, Cloud Armor, Pub/Sub, GCS, Cloud CDN, KMS)



IaC & CI/CD:
Terraform, Terraspace, GitHub Actions, OPA/Conftest



Languages:
Python, , Bash, YAML/HCL



Containers:
Docker, Kubernetes



Database:
MongoDB, Cloud SQL

Ideal Profile


• Good understanding of
cloud services
(GCP preferred, AWS/Azure transferable).


• Familiarity with
Linux, Docker, and Git
.


• Curious about
ML infrastructure and automation
.



Eager to learn
under mentorship from a Lead MLOps Engineer.


• Good
communication and documentation
skills in English.

What You'll Learn


• Design and deploy
production-grade infrastructure
on GCP.


• Build
CI/CD pipelines
and
Infrastructure as Code
with Terraform.


• Implement
observability
with SLOs, distributed tracing, and monitoring dashboards.


• Manage
MLOps workflows
(experiment tracking, model versioning, GPU workloads).


• Apply
cloud security best practices
(IAM, encryption, compliance).


• Optimize
costs and performance
in a regulated medical AI environment.


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