Cloud Infrastructure Engineer: Multi-Cloud, Kubernetes, AI

datologyai; Redwood City (CA); On-site; USD 180,000 - 300,000; Full time

Benefits offered by this job

Health benefits

401(k) plan with 4% company match

Unlimited PTO

Parental Leave: 12 weeks + 6 months Wf

Wellness stipend

Learning and development stipend

Lunches and snacks provided

Relocation assistance

Job summary

DatologyAI is hiring a Cloud Infrastructure Engineer to design, build, and run scalable, secure multi-cloud infrastructure powering our ML data curation and model training pipelines. You will work with engineering, research, and product teams to deploy compute resources across AWS and other clouds.

You will design infrastructure-as-code, manage Kubernetes clusters, optimize CI/CD, and implement robust monitoring and security. This is a hands-on role in a fast-moving startup environment.

Qualifications

• 4+ years of relevant infrastructure experience.
• Led or contributed to robust infrastructure at a startup or fast-moving org.
• Deep cloud experience (AWS), multi-cloud/hybrid setups preferred.
• Strong Kubernetes, Terraform, and containerized architectures.

Responsibilities

• Architect and maintain multi-cloud infrastructure (AWS, Azure, GCP).
• Define infrastructure-as-code best practices (Terraform, CloudFormation, Pulumi).
• Design and manage Kubernetes-based systems for training, inference, and data processing workloads.
• Optimize CI/CD pipelines and deployment across environments.
• Build monitoring, alerting, and logging for high availability and observability.
• Provide infra support for large-scale ML training and inference workloads.
• Ensure deployment models (cloud, on-prem, hybrid) meet enterprise needs.

Skills

Kubernetes

Terraform

Go

Python

Bash

Cloud infrastructure

Multi-cloud

Security

Tools

AWS

Kubernetes

Terraform

Job description

DatologyAI is hiring a Cloud Infrastructure Engineer to design, build, and run scalable, secure multi-cloud infrastructure powering our ML data curation and model training pipelines. You will work with engineering, research, and product teams to deploy compute resources across AWS and other clouds.

You will design infrastructure-as-code, manage Kubernetes clusters, optimize CI/CD, and implement robust monitoring and security. This is a hands-on role in a fast-moving startup environment.