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AI Platform Engineer

1 month ago
India
Full-time
Mid Level
Machine Learning Engineer
DevOps and Infrastructure
Ansible AWS Azure CI/CD Docker GCP Kubeflow Kubernetes MLflow MLOps Python PyTorch TensorFlow Terraform
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AHEAD

AHEAD

AHEAD accelerates the impact of technology on clients by engineering customized data, developer, and infrastructure platforms that improve IT operations. By weaving together cloud infrastructure, intelligent operations, and modern applications, we help...

IT Services
1K-5K
$43M raised
View All Jobs 10

Description

  • Architect and manage Kubernetes clusters tailored to AI/ML workloads.
  • Implement GPU resource orchestration and workload scheduling using Run:ai or similar operators.
  • Develop and maintain Python-based automation scripts and end-to-end ML pipelines.
  • Automate infrastructure provisioning with Terraform and manage configuration with Ansible.
  • Create and manage Jupyter Notebooks and environments to support experimentation and collaboration.
  • Integrate and optimize NVIDIA Enterprise Suite components (CUDA, NeMo, 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).
  • Collaborate with data scientists and platform engineers to drive efficient resource utilization and to deploy and scale AI workloads across cloud, hybrid, and HPC environments.

Requirements

  • 4+ years in platform or solutions architecture, with 2+ years focused on AI/ML workloads.
  • Strong proficiency in Python and practical experience with ML frameworks such as TensorFlow and PyTorch.
  • Hands-on experience with Kubernetes and container orchestration.
  • Familiarity with Run:ai or similar GPU scheduling platforms for GPU workload management.
  • Expertise in infrastructure automation using Terraform and configuration management with Ansible.
  • Experience using Jupyter Notebooks for ML development and collaboration.
  • Knowledge of NVIDIA Enterprise Suite components (CUDA, NeMo Framework, Triton, TensorRT, GPU drivers) and GPU acceleration optimization.
  • Solid understanding of MLOps principles and tools (e.g., MLflow, Kubeflow) for CI/CD and model monitoring.
  • Experience with high-performance computing (HPC), distributed training, and model optimization techniques.
  • Certifications in Kubernetes or cloud platforms (AWS, Azure, GCP) are preferred.

Benefits

  • Remote role based in India (remote work option).
  • Access to a multi-million-dollar lab and top-notch technology resources.
  • Sponsored certifications and support for continued learning and cross-department training and development.
  • Active diversity and inclusion programs (e.g., Moving Women AHEAD, RISE AHEAD).
  • Supportive culture that funds professional growth and skills development.

Interested in this position?

Apply directly on the company website

Apply Now

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