Implementation Specialist (GenAI Servers)

  • Posted 48 days ago | Updated 18 hours ago

Overview

Contract - W2
Contract - 5 day((s))

Skills

Python
Scripting
Performance Tuning
Deployment
GCP
Terraform
Large-Scale
Linux
Provisioning
Optimization

Job Details

Position: Sr. Implementation Specialist (GenAI Servers)

  • Experience: 8 to 10 years
  • Location: Remote (United States )
  • Duration ; 12 Months

Job Description:

We are seeking a Sr. Implementation Specialist with expertise in GenAI server deployments, AI infrastructure, and large-scale AI model integration. The ideal candidate will have a strong background in AI/ML architecture, server configurations, and cloud-based AI solutions.

Key Responsibilities:

  • Lead the implementation, deployment, and optimization of GenAI servers in cloud and on-prem environments.
  • Work closely with AI/ML teams to support LLMs, RAG models, and AI pipelines on dedicated AI hardware.
  • Manage server provisioning, performance tuning, and scaling AI workloads.
  • Integrate AI models with NVIDIA GPUs, TPUs, and specialized AI accelerators.
  • Optimize storage, compute, and network configurations for GenAI applications.
  • Automate server setup, monitoring, and security compliance for AI-driven workloads.
  • Work with MLOps and DevOps teams to streamline AI model deployments.
  • Troubleshoot and resolve performance bottlenecks in distributed AI architectures.

Required Skills:

AI/ML Infrastructure Strong experience in setting up and managing GenAI servers.
Cloud Expertise Experience with AWS, Azure, Google Cloud Platform AI services, or on-prem AI clusters.
GPU/TPU Optimization Hands-on knowledge of NVIDIA CUDA, TensorRT, and AI accelerators.
Server Implementation Proficiency in Linux-based AI server deployments.
ML Model Integration Experience integrating LLMs, RAG models, and Transformer-based architectures.
Scripting & Automation Proficiency in Python, Bash, Terraform, or Ansible for AI infrastructure automation.
Performance Tuning Experience optimizing compute, memory, and network configurations for AI workloads.

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