Python AIML Engineer

Role & responsibilities

Python and Kubernetes to design and manage scalable infrastructure using tools like KEDA, Grafana, and Karpenter. The role focuses on GPU-accelerated workloads and CI/CD pipelines, with a nice-to-have knowledge of AI/ML deployment.

2 Positions

Key Responsibilities

Deploy and manage Kubernetes clusters, using Karpenter for node autoscaling.

Implement event-driven scaling with KEDA for dynamic workloads.

Build Grafana dashboards for monitoring with Prometheus integration.

Optimize GPU resources in Kubernetes for compute-intensive tasks.

Develop Python scripts for automation, IaC, and CI/CD pipelines.

Manage containerized deployments with Docker and Helm.

Troubleshoot production issues to ensure high availability.

Collaborate on CI/CD workflows using Jenkins, GitLab.

Mentor junior engineers and enforce DevOps best practices.

Good-to-Have Skills

Understanding of AI architecture (neural networks, inference).

Golang proficiency for high-performance tools.

Experience with ML deployment (Kubeflow, TensorFlow Serving).

Familiarity with ML model development (scikit-learn, PyTorch).