Junior Cloud Engineer

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
- 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
  - letter queues.
• Build Infrastructure as Code with Terraform/Terraspace and CI/CD pipelines (Git
  Hub Actions).
• Optimize storage & costs: lifecycle policies, tiered storage, resource labeling, budget monitoring.
• Support compliance initiatives: audit logs, retention policies,
  - 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)
• Ia
  C & CI/CD: Terraform, Terraspace, Git
  Hub Actions, OPA/Conftest
• Languages: Python, Node. js, Bash, YAML/HCL
• Containers: Docker, Kubernetes
• Database: Mongo
  DB, Cloud SQL

Ideal Profile

• 0–2 years of experience in Dev
  Ops or Cloud.
• Basic 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.

Seniority level

Entry level

Employment type

Full-time

Job function

Engineering and Information Technology

Industries

Medical Equipment Manufacturing