Senior Cloud Engineer

Pragmatike•Cambridge, MA

About The Position

Pragmatike is hiring on behalf of a fast-growing AI startup recognized as a Top 10 GenAI company by GTM Capital , founded by MIT CSAIL researchers . We are searching for a Senior Cloud Engineer (Multicloud) with deep, hands-on experience building, operating, and scaling production infrastructure across AWS, GCP, and Azure . You will work directly on the cloud and platform layer supporting large-scale, distributed AI systems used by Fortune 500 customers . This role is ideal for an engineer who has operated real multicloud environments in production—not someone limited to a single provider. You will be responsible for building reliable, scalable systems while navigating the complexity of differing cloud primitives, networking models, and operational trade-offs.

Requirements

5+ years of experience as a Cloud / Platform / Infrastructure Engineer .
Hands-on production experience with AWS, GCP, and Azure (deep expertise in at least one).
Strong experience running Kubernetes in production across multiple clouds.
Strong Terraform experience managing multicloud infrastructure.
Solid understanding of cloud networking differences and security models across providers.
Experience operating distributed systems with on-call ownership.
Ability to work across provider-specific services while maintaining consistent abstractions.

Nice To Haves

Experience supporting AI/ML or data-intensive workloads in production.
Exposure to GPU-enabled cloud infrastructure or high-performance compute.
Experience with CI/CD automation and release pipelines.
Familiarity with compliance requirements (SOC 2, ISO 27001).
Startup experience or comfort in fast-moving, ambiguous environments.

Responsibilities

Build, deploy, and operate production infrastructure across AWS, GCP, and Azure .
Maintain consistent environments using Infrastructure as Code (Terraform preferred).
Deploy and operate Kubernetes clusters and containerized workloads across multiple cloud providers.
Design and manage cloud networking (VPC/VNet design, peering, load balancing, private connectivity).
Implement monitoring, logging, alerting, and incident response for multicloud systems.
Optimize performance, reliability, and cost across providers through autoscaling and capacity planning.
Support AI training and inference workloads in multicloud environments.
Troubleshoot complex production issues spanning compute, networking, storage, and Kubernetes layers.
Collaborate closely with AI, backend, and platform teams to support production systems.