Agentic Engineering

• Identify AI use cases that boost efficiency and let us scale to more teams and partners without proportional effort.
• Pinpoint manual work that grows with every new team or partner, such as onboarding, provisioning, and support requests.
• Design and build AI agents for the selected use cases, from prototype to production.
• Embed agents into CI/CD pipelines, provisioning workflows, and the cloud runtime.
• Operate, monitor, and maintain agents in production.
• Enforce governance, guardrails, and human-in-the-loop approval for agent actions.
• Measure efficiency and scalability gains, and continuously improve agent quality, reliability, and cost.

Cloud Infrastructure & Platform Operations

• Design, deploy, and maintain cloud infrastructure on Azure or AWS.
• Operate Kubernetes and container platforms for application and AI workloads.
• Automate infrastructure provisioning with Infrastructure as Code (IaC).
• Build and maintain Helm charts for application deployments.
• Troubleshoot infrastructure, networking, and deployment issues.

DevOps & Automation

• Build and maintain CI/CD pipelines with Azure DevOps, GitHub Actions, or similar tools.
• Automate operational tasks with PowerShell and C#.
• Drive release management, deployment automation, and environment provisioning.
• Use AI-powered tools to increase operational efficiency.

AI & MLOps Support

• Deploy and monitor AI/ML applications and services.
• Maintain MLOps workflows with Azure ML, MLflow, or Kubeflow.
• Monitor performance of AI workloads and their infrastructure.

Monitoring & Reliability

• Implement monitoring and alerting with Prometheus, Grafana, ELK Stack, or cloud-native tools.
  • Resolve incidents, run root cause analyses, and optimize performance.