. Infrastructure Support & Environment Management

• Set up and maintain compute infrastructure, including GPU-enabled environments.
• Configure and manage Linux-based systems for development and production environments.
• Provisioning and configuration of cloud and on-prem infrastructure.
• Monitor system resources and assist in performance tuning and optimization.

2. Containerization & Deployment

• Build and manage containerized applications using Docker.
• Deploy and manage applications on Kubernetes clusters under guidance from senior engineers.
• Creating deployment configurations, Helm charts, and environment setups.
• Support scaling and orchestration of microservices and AI workloads.

3. CI/CD Pipeline Implementation

• Develop and maintain CI/CD pipelines for application and AI model deployment.
• Automate build, test, and deployment processes using tools like Azure DevOps, GitHub Actions, or Jenkins.
• Ensure smooth promotion of code and models across environments (dev, test, prod).
• Troubleshoot pipeline failures and deployment issues.

4. MLOps & AI Deployment Support

• Deploying machine learning models and LLM-based services.
• Integration of AI components into production systems.
• Contribute to model versioning, monitoring, and lifecycle management.
• Work with AI engineers to operationalize RAG pipelines and inference services.

5. Monitoring, Logging & Issue Resolution

• Implement and maintain monitoring and logging solutions (e.g., Prometheus, Grafana, ELK).
• Track application performance, system health, and availability.

. Infrastructure Support & Environment Management

• Set up and maintain compute infrastructure, including GPU-enabled environments.
• Configure and manage Linux-based systems for development and production environments.
• Provisioning and configuration of cloud and on-prem infrastructure.
• Monitor system resources and assist in performance tuning and optimization.

2. Containerization & Deployment

• Build and manage containerized applications using Docker.
• Deploy and manage applications on Kubernetes clusters under guidance from senior engineers.
• Creating deployment configurations, Helm charts, and environment setups.
• Support scaling and orchestration of microservices and AI workloads.

3. CI/CD Pipeline Implementation

• Develop and maintain CI/CD pipelines for application and AI model deployment.
• Automate build, test, and deployment processes using tools like Azure DevOps, GitHub Actions, or Jenkins.
• Ensure smooth promotion of code and models across environments (dev, test, prod).
• Troubleshoot pipeline failures and deployment issues.

4. MLOps & AI Deployment Support

• Deploying machine learning models and LLM-based services.
• Integration of AI components into production systems.
• Contribute to model versioning, monitoring, and lifecycle management.
• Work with AI engineers to operationalize RAG pipelines and inference services.

5. Monitoring, Logging & Issue Resolution

• Implement and maintain monitoring and logging solutions (e.g., Prometheus, Grafana, ELK).
• Track application performance, system health, and availability.