Work from Office
Full Time
Cloud Infrastructure & Automation
Design and maintain scalable, secure, and cost-effective cloud infrastructure (Azure / AWS) for AI/ML solutions.
Use Infrastructure-as-Code (IaC) tools like Terraform or ARM Templates for provisioning and configuration management.
CI/CD & MLOps Pipelines
Implement and maintain CI/CD pipelines for AI model training, testing, and deployment using Azure DevOps, GitHub Actions, Jenkins, or GitLab CI.
Integrate ML lifecycle tools such as MLflow, Kubeflow, SageMaker Pipelines, or Azure ML Pipelines.
Model Deployment & Monitoring
Containerize AI/GenAI models using Docker and orchestrate them using Kubernetes or Azure Kubernetes Service (AKS) / EKS.
Set up model monitoring and logging for drift detection, performance, and reliability.
Security, Compliance, and Governance
Ensure secure deployment and access control using IAM policies, TLS, secret management (e.g., HashiCorp Vault, AWS Secrets Manager).
Implement logging, monitoring, and auditing practices aligned with enterprise security standards.
Collaboration & Support
Work closely with data science and application teams to support rapid experimentation and production rollouts.
Provide environment setup, incident resolution, and performance optimization.
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