AI Platform Engineer
A
AHEADAI/ML infrastructure
Listing location: India; Workplace type: Remote; Structured job location: IndiaFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 4+ years in platform architecture or solutions architecture, with 2+ years focused on AI/ML workloads.
- Required Skills
- PythonKubernetesPyTorchTensorflowTerraformAnsibleMLOps
Requirements
- Have 4+ years of experience in platform architecture or solutions architecture, including 2+ years focused on AI/ML workloads.
- Demonstrate strong proficiency in Python.
- Have experience with TensorFlow or PyTorch.
- Have hands-on experience with Kubernetes and container orchestration.
- Be familiar with Run:ai or similar GPU scheduling platforms.
- Have expertise in Terraform and Ansible for infrastructure automation.
- Have experience using Jupyter Notebooks for ML development.
- Know NVIDIA Enterprise Suite components, including CUDA, NeMo Framework, Triton, and GPU drivers.
- Understand MLOps principles and tools such as MLflow and Kubeflow.
- Have a background deploying and scaling AI workloads in cloud or hybrid environments.
- Have experience with high-performance computing (HPC) environments.
- Be familiar with distributed training and model optimization techniques.
- Hold a Kubernetes or cloud platform certification (AWS, Azure, or GCP).
Responsibilities
- Architect and manage Kubernetes clusters tailored to AI/ML workloads.
- Implement Run:ai and operators for GPU resource orchestration and workload scheduling.
- Develop and maintain Python-based automation scripts and ML pipelines.
- Automate infrastructure provisioning with Terraform and configuration management with Ansible.
- Create and manage Jupyter Notebooks for experimentation and collaboration.
- Integrate and optimize NVIDIA Enterprise Suite components, including CUDA, NeMo Framework, Triton, TensorRT, and GPU drivers.
- Establish and maintain MLOps practices for model lifecycle management, CI/CD, and monitoring.
- Collaborate with data scientists and platform engineers to support resource utilization and scalability across cloud and hybrid environments.
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