Cloud Engineer
3 semanas atrás
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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