DevOps Engineer

Hyrhub; United States; Remote; USD 120,000 - 170,000; Full time

Job summary

Hyrhub is seeking an experienced DevOps/CI–CD engineer to design and maintain deployment pipelines for our enterprise platform, AI agents, and supporting services. You will automate build, test, packaging, release, and deployment across environments, manage configuration and IaC, and ensure secure handling of credentials and environment settings.

You will implement logging, monitoring, and observability, define reliable deployment strategies with rollback and versioning, and collaborate with

Responsibilities

• Design and maintain CI/CD pipelines and deployment processes for enterprise platform, AI agents, applications, and supporting services.
• Automate build, testing, packaging, release, and deployment processes across environments.
• Manage configuration and environment-specific settings across development, testing, staging, and production.
• Implement infrastructure-as-code and automation patterns where applicable.
• Support deployment of AI services, MCP components, APIs, backend services, and application
• Establish secure management of credentials, secrets, certificates, and environment configurations.
• Implement logging, monitoring, alerting, and observability for deployed AI solutions.
• Define deployment strategies that support reliability, rollback, versioning, and controlled releases.
• Troubleshoot pipeline, infrastructure, environment, and deployment issues.
• Partner with security and platform teams to ensure deployments comply with enterprise standards.
• Enable repeatable deployment patterns that can be reused across multiple AI agents and use cases.
• Support operational readiness, production releases, and ongoing platform reliability.

Job description

Driving Innovation, Empowering Insights

• Design and maintain CI/CD pipelines and deployment processes for enterprise platform, AI agents, applications, and supporting services.
• Automate build, testing, packaging, release, and deployment processes across environments.
• Manage configuration and environment-specific settings across development, testing, staging, and production.
• Implement infrastructure-as-code and automation patterns where applicable.
• Support deployment of AI services, MCP components, APIs, backend services, and application
• Establish secure management of credentials, secrets, certificates, and environment configurations.
• Implement logging, monitoring, alerting, and observability for deployed AI solutions.
• Define deployment strategies that support reliability, rollback, versioning, and controlled releases.
• Troubleshoot pipeline, infrastructure, environment, and deployment issues.
• Partner with security and platform teams to ensure deployments comply with enterprise standards.
• Enable repeatable deployment patterns that can be reused across multiple AI agents and use cases.
• Support operational readiness, production releases, and ongoing platform reliability.