Gather AI - Cloud Engineer (Platform & Infrastructure)
Requirements
• Bachelor's degree in Computer Science, Engineering, or a related field • 5+ years of experience operating production cloud infrastructure at scale • Deep experience with at least one major cloud provider (Azure or AWS) and working familiarity with the other • Hands-on experience with Kubernetes and Docker for running containerized workloads in production environments • Proficiency with Terraform or equivalent Infrastructure-as-Code tooling for provisioning and managing cloud infrastructure • Experience implementing automated deployment pipelines using tools such as GitHub Actions, GitLab CI, or similar platforms • Strong operational mindset with a focus on reliability, automation, and clear technical documentation • Experience with observability tooling such as Prometheus, ELK, OpenTelemetry, or similar logging, metrics, and monitoring systems • Familiarity supporting ML infrastructure workloads including pipeline orchestration, model deployment, and scalable inference environments • Experience working in logistics, robotics-adjacent platforms, or real-time distributed systems • Track record of translating application requirements into secure, reliable, and operationally safe infrastructure architecture • Exposure to cloud cost visibility and optimization practices • Experience introducing infrastructure governance standards including templates, security baselines, and operational documentation
Responsibilities
• Review and rationalize current Azure and AWS environments, identifying configuration drift, security gaps, and operational inconsistencies, and establish clear configuration standards across cloud accounts • Introduce repeatable Infrastructure-as-Code patterns to ensure cloud resources are provisioned, versioned, and audited through automated workflows • Strengthen CI/CD pipelines for infrastructure and application deployment to reduce manual operations and increase release safety across both application services and ML workloads • Establish consistent logging, metrics, and alerting practices across infrastructure and container workloads to improve operational visibility • Audit and improve cloud security practices including IAM policies, secrets management, network segmentation, and operational access controls • Evaluate current infrastructure architecture and introduce patterns that enable workloads to operate portably across both Azure and AWS environments • Improve Kubernetes platform reliability by refining autoscaling policies, workload isolation, and cluster lifecycle management • Partner with Fullstack and ML teams to reduce infrastructure friction around environments, networking, and resource provisioning
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