AI Engineer

Batch: 2023/2024/2025/2026. Responsibilities: - Deploy, update, restart, and manage GPU-based AI applications and services. - Monitor AI services, APIs, application logs, performance, and availability. - Monitor GPU health, utilization, VRAM, temperature, power usage, and processes. - Monitor CPU, RAM, disk, and other infrastructure resources. - Troubleshoot application crashes, deployment failures, GPU issues, and service-related problems. - Perform Linux server health checks and troubleshooting. - Manage and troubleshoot Docker containers for AI applications. - Maintain deployment configurations and operational procedures. - Develop basic Python/Shell scripts for monitoring, health checks, and automation. Requirements: - Strong troubleshooting and analytical skills with a structured approach to problem-solving. - Comfortable working with Linux, logs, monitoring tools, and command-line environments. - Hands-on exposure to NVIDIA GPUs and GPU monitoring tools. - Familiarity with AI/LLM applications and AI serving platforms such as vLLM. - Working knowledge of Docker and Kubernetes. - Exposure to Prometheus and Grafana. - Basic understanding of Git/version control workflows. - Strong interest in AI, GPU infrastructure, and LLM technologies. - Ability to take ownership of assigned tasks and work independently. - Willingness to continuously learn and grow in AI infrastructure and operations. Note: Share your CV.