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Remote mid-level metro DevOps role with generalist title and broad skills creates high applicant competition.
Specialized AI-agent, LLM gateway, and enterprise ERP platform experience creates strong domain bias, reducing transferability.
Explicit 4–8 years and many mandatory Kubernetes, IaC, CI/CD, and LLM/AI platform requirements increase shortlist strictness.
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Design, deploy, and operate Kubernetes-based platform infrastructure and AI deployment environments for enterprise AI services.
Build and maintain Infrastructure as Code, CI/CD pipelines, GitHub governance, and ensure secure, scalable deployments of AI agents, GenAI, and LLM gateway platforms.
Implement monitoring, reliability, security, and governance controls including secrets management, operational automation, and incident response for enterprise AI and cloud services.
4–8 years of experience in DevOps, Platform Engineering, SRE, MLOps, Cloud Engineering, or similar roles.
Expertise in Kubernetes, Helm, Docker, Infrastructure as Code (Terraform, Pulumi), GitHub Enterprise and Actions, AI agent operations, and LLM gateway platforms.
Experience managing production Kubernetes environments on AWS, Azure or GCP with CI/CD pipeline automation and GitOps practices.
Bachelor’s degree in Computer Science, Engineering or related field; relevant certifications like CKA/CKAD, AWS/Azure/GCP preferred.
Hands-on with production Kubernetes platform operations, GitHub-based engineering controls, and enterprise AI services involving GenAI, MCP, RAG, and LLM gateways.
Experienced operating AI platform infrastructure and workflows supporting multi-cloud and air-gapped client environments with strong focus on reliability, security, and developer productivity.
Familiar with AI model integrations (OpenAI, Anthropic, Gemini), workflow orchestration, AI observability tools, and infrastructure automation in a large scale enterprise SaaS or AI platform context.