AI Infrastructure Engineer GPU @ Applied Infoservices

Job Description

Job Title: AI Infrastructure Engineer GPU & Kubernetes Specialist Location: Hyderabad Office Type: Fulltime Job Summary: We are seeking a highly skilled and motivated HPC and AI Workloads Specialist with expertise in GPU-based environments and Kubernetes. The successful candidate will play a critical role in designing, deploying, and optimizing HPC and AI workloads on cloud and on-premises infrastructure. This position requires a deep understanding of GPU compute technologies, Kubernetes orchestration, and performance tuning. Key Responsibilities: Design, implement, and optimize HPC and AI workloads leveraging GPU-based environments. Manage and deploy Kubernetes clusters across cloud platforms (AWS, Azure, GCP) or on-premises environments. Develop and maintain automated deployment pipelines for AI and HPC workloads. Optimize GPU resource allocation and utilization for large-scale compute environments. Collaborate with development and research teams to deliver scalable and efficient computing solutions. Troubleshoot and resolve performance and scalability issues in distributed computing environments. Stay up-to-date with the latest technologies and best practices in HPC, AI, and container orchestration. Required Skills and Qualifications: Proven expertise in High-Performance Computing (HPC) and AI workloads within GPU-based environments. Hands-on experience with Kubernetes (any cloud platform: AWS, Azure, GCP) for container orchestration. Proficiency in GPU compute technologies, including CUDA, NVIDIA GPUs, and related frameworks. Strong understanding of cloud computing infrastructure and hybrid cloud solutions. Experience with automation and configuration management tools (e.g., Helm, Terraform, Ansible). Preferred Certifications: NVIDIA Certified for GPU Compute HPC Certifications for cluster management and compute optimization. CNCF Kubernetes Certifications (CKA, CKS) or Red Hat OpenShift Certification. ,

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Keyskills:   Kubernetes CUDA automation configuration management