AI Solutions Deployment Specialist

18 hours ago

Bengaluru, Karnataka, India beBeeExpert Full time ₹ 15,00,000 - ₹ 25,00,000
AI Solutions Deployment Specialist

Job Description:

We are seeking an experienced AI solutions deployment specialist to lead the transition of AI Proof-of-Concepts (PoCs) into production environments. The ideal candidate will have a strong background in deploying AI/ML models using Nvidia GPUs in enterprise production environments.

Key Responsibilities:

  • Lead end-to-end transitions of AI PoCs into production environments, managing the entire process from testing to final deployment.
  • Configure, install, and validate AI systems using key platforms, including VMware ESXi and vSphere for server virtualization, Linux (Ubuntu/RHEL) and Windows Server for operating system integration, Docker and Kubernetes for containerization and orchestration of AI workloads.
  • Conduct comprehensive performance benchmarking and AI inferencing tests to validate system performance in production.
  • Optimize deployed AI models for accuracy, performance, and scalability to ensure they meet production-level requirements and customer expectations.
  • Serve as the primary technical lead/SME for the AI POC deployment in enterprise environments, focusing on AI solutions powered by Nvidia GPUs.

Requirements:

The successful candidate will have:

  • Overall experience of 7-10 years
  • Relevant experience of 2-4 years in deploying AI/ML models/AI solutions using Nvidia GPUs in enterprise production environments
  • Demonstrated success in leading and managing complex AI infrastructure projects, including PoC transitions to production at scale

Technical Expertise:

The ideal candidate will have experience in:

  • Retrieval Augmented Generation (RAG), NVIDIA AI Enterprise, NVIDIA Inference Microservices (NIMs), Model Management, Kubernetes
  • Extensive experience with Nvidia AI Enterprise, GPU-accelerated workloads, and AI/ML frameworks such as PyTorch and TensorFlow
  • Proficient in deploying AI solutions across enterprise platforms, including VMware ESXi, Docker, Kubernetes, and Linux (Ubuntu/RHEL) and Windows Server environments
  • MLOps proficiency with hands-on experience using tools such as Kubeflow, MLflow, or AWS SageMaker for managing the AI model lifecycle in production
  • Strong understanding of virtualization and containerization technologies to ensure robust and scalable deployments

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