AI Infrastructure Engineer
Skills
About This Role
Overview
Eram Talent is looking for a talented AI Infrastructure Engineer to join our innovative team.
Key Responsibilities
- Design, implement, and manage high-performance computing environments tailored for AI and machine learning applications.
- Deploy and maintain GPU-accelerated clusters, cloud-based AI platforms, and parallel processing systems.
- Collaborate with data scientists and ML engineers to understand infrastructure requirements for various AI projects.
- Optimize resource allocation and scalability of AI infrastructure to support large datasets and complex models.
- Automate infrastructure provisioning and deployment using Infrastructure as Code (IaC) tools.
- Ensure security, compliance, and reliability of AI infrastructure.
- Monitor system performance and troubleshoot issues to minimize downtime and maximize productivity.
- Stay updated on emerging technologies and best practices in AI infrastructure and propose continuous improvements.
- Bachelor s or higher degree in Computer Science, Engineering, or related technical field.
- 3+ years of experience in infrastructure engineering, preferably with a focus on AI, machine learning, or high-performance computing environments.
- Cloud skills - GCP/OpenShift, Kubernetes (k8s), Docker containers/images
- AI skills Model training, testing/evaluation, deployment
- ML/LLMOPs
- LLMs and GenAI core skills how do LLMs work under the hood, inference mechanics of LLMs/GenAI
- Inference scaling, distributed computing, inference benchmarking, inference planning for meeting SLAs/SLOs
- GPUs and how to work with them, distributed workloads handling, autoscaling
• NVIDIA NIMs, Huggingface
- NVIDIA Superpods (HPC, slurm, k8s)
- Monitoring, dashboards for LLM/ML workloads and applications
- AI Application Architecture know-how, end to end flows
- DevOps (CI/CD, argoCD, git, Jenkins etc)
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