Multi Cloud Engineer || Remote || W2 Role

Required Qualifications

• 6+ years experience troubleshooting and managing core services in both AWS and Google Cloud Platform, including compute, networking, identity, storage, monitoring/observability (e.g., CloudWatch, Cloud Monitoring), and security (e.g., IAM, KMS/Cloud KMS, network security controls).
• 3+ years developing automation solutions using Python, Infrastructure-as-Code (e.g., Terraform, CloudFormation, Google Deployment Manager), APIs, workflows, or orchestration tools.
• 3+ years of experience supporting enterprise infrastructure, cloud operations, managed services, or platform engineering environments.
• Demonstrated ability to leverage AI tools, assistants, and automation platforms to improve operational efficiency and accelerate problem resolution.
• Experience consuming and integrating REST APIs and cloud-native services.
• Strong analytical and systems-thinking skills with a proven ability to diagnose complex technical issues.
• Strong written and verbal communication skills.
• Experience supporting containerized workloads.
• At least one current AWS or Google Cloud Platform certification (e.g., AWS Certified Solutions Architect, AWS Certified SysOps Administrator, Google Cloud Professional Cloud Architect), or willingness to obtain one within 90 days of hire.
• Must be eligible to support public sector customers and successfully complete any required criminal history, fingerprint-based, or other customer-mandated background investigations necessary to access customer environments and systems.

Preferred Qualifications

• Experience working in various roles at an AWS/Google Cloud Platform MSP
• Hands-on production experience across all three major hyperscalers (AWS, Azure, and Google Cloud Platform), including core compute, networking, identity, and security services.
• Experience building AI-powered operational workflows and intelligent automation solutions.
• Experience leveraging LLMs, generative AI platforms, and AI assistants within engineering or operations environments.
• Experience integrating AI services such as Amazon Bedrock, Azure OpenAI, Anthropic Claude, OpenAI APIs, or similar platforms.
• Experience developing agentic workflows, AI orchestration frameworks, or autonomous operational processes.
• Experience with AIOps platforms, observability tools, and intelligent event correlation systems.
• Experience building knowledge retrieval, RAG, or AI-assisted support solutions.
• Experience with cost optimization and rightsizing practices across AWS and Google Cloud Platform (e.g., AWS Cost Explorer/Trusted Advisor, Google Cloud Platform Cost Management/Recommender).
• Experience with observability and troubleshooting of complex  EKS/AKS/GKE implementations.