Ml Engineer

Job Description*

Role & Responsibilities:

• Deploy and manage Kubernetes clusters with autoscaling using Karpenter.
• Implement event-driven scaling with KEDA for dynamic workloads.
• Optimize GPU workloads for compute-intensive tasks in Kubernetes.
• Build and maintain Grafana dashboards with Prometheus integration.
• Develop Python scripts for automation, IaC, and CI/CD pipelines.
• Manage containerized deployments with Docker and Helm.
• Collaborate on CI/CD workflows using Jenkins and GitLab.
• Troubleshoot production issues ensuring high availability.
• Mentor junior engineers and enforce DevOps best practices.

Must-Have Skills:

• Golang (Mandatory) and Python expertise.
• Strong hands-on with KEDA, Kubernetes, Docker, Helm.
• CI/CD exposure with Jenkins / GitLab.
• Knowledge of Grafana, Prometheus for monitoring.

Good-to-Have Skills:

• AI/ML architecture knowledge (neural networks, inference).
• Experience with ML deployment tools (Kubeflow, TensorFlow Serving).
• Familiarity with ML model development (PyTorch, scikit-learn).