AI Platform Engineer
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Job Description
AI Platform Engineer optimizing AI/ML infrastructure and workflows at AHEAD. Collaborating with data scientists to build scalable environments for ML models.
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 (CUDA, NeMo Framework, Triton, TensorRT, GPU drivers) for accelerated computing.
- Establish and maintain MLOps best practices for model lifecycle management, CI/CD, and monitoring (e.g., MLflow, Kubeflow).
- Work closely with data scientists and platform engineers to ensure efficient resource utilization and scalability across environments.
Requirements:
- 4+ years in platform architecture or solutions architecture, with 2+ years focused on AI/ML workloads.
- Experience with high-performance computing (HPC) environments.
- Familiarity with distributed training and model optimization techniques.
- Certification in Kubernetes or cloud platforms (AWS, Azure, GCP).
- Strong proficiency in Python and experience with ML frameworks (TensorFlow, PyTorch).
- Hands-on experience with Kubernetes and container orchestration.
- Familiarity with Run:ai or similar GPU scheduling platforms.
- Expertise in Terraform and Ansible for infrastructure automation.
- Experience with Jupyter Notebooks for ML development.
- Knowledge of NVIDIA Enterprise Suite (CUDA, NeMo Framework, Triton, GPU drivers).
- Solid understanding of MLOps principles and tools (e.g., MLflow, Kubeflow).
- Background in deploying and scaling AI workloads in cloud or hybrid environments.
Benefits:
- India Employment Benefits include:
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