DevOps Engineer- "AI Skills"

Required Skills

• 8+ years of overall IT experience with at least 5+ years of hands-on experience in Azure Cloud engineering and operations.
• Strong expertise in Azure Networking, including VNets, VNet Peering, NSGs, UDRs, Private Endpoints, DNS, Load Balancers, ExpressRoute, and VPN connectivity.
• 6+ years of experience with AKS (Azure Kubernetes Service), including cluster deployment, upgrades, ingress management, networking, scaling, security, and troubleshooting.
• 6+ years of experience with Azure Application Gateway and Azure Front Door, including WAF configuration, SSL/TLS termination, path-based routing, global load balancing, and high-availability architectures.
• 6+ years of experience with Infrastructure as Code (Terraform), including module development, remote state management, CI/CD integration, and multi-environment deployments.
• 6+ years of experience with GitHub Actions for CI/CD pipelines, automated deployments, security scanning, and release management.
• Strong experience with Kubernetes, Docker, containerized applications, and microservices architectures.

Preferred Skills

• 4+ years of experience with Dynatrace for application performance monitoring, distributed tracing, and observability.
• 4+ years of experience with Splunk for log analytics, monitoring, dashboard creation, and operational troubleshooting.
• Experience with AWS cloud services is good to have.
• Experience designing and operating multi-region, highly available cloud architectures.
• Knowledge of DevSecOps practices, container security, vulnerability management, and compliance requirements.
• Experience working in Healthcare, Health Insurance, or Healthcare Technology domains.

AI Skills

All contractor resources are expected to demonstrate baseline proficiency in enterprise-approved AI tools as part of their day-to-day responsibilities. This includes, but is not limited to:

Consistent Use: Maintain a minimum of 90% weekly usage of AI tools such as GitHub Copilot, Microsoft 365 Copilot, and other GenAI platforms approved by the enterprise.

Applied Productivity: Leverage AI tools to enhance coding, documentation, data analysis, and decision-making workflows.

Continuous Learning: Stay current with evolving AI capabilities and features, and apply them to improve delivery quality and velocity.