Job Description

Design, build, and maintain scalable infrastructure to support AI and Machine Learning workloads. Deploy and manage AI/ML platforms across cloud, on-premises, or hybrid environments. Configure and manage GPU-based infrastructure for model training, inference, and AI workloads. Implement and maintain containerized AI environments using Docker and Kubernetes. Build and manage MLOps pipelines to automate model development, deployment, monitoring, and lifecycle management. Deploy and manage AI model serving and inference platforms. Implement infrastructure automation using Infrastructure as Code (IaC) tools. Configure cloud-based AI infrastructure and services across AWS, Microsoft Azure, or Google Cloud. Optimize infrastructure performance, scalability, availability, and cost for AI workloads. Implement monitoring, logging, security, and resource management for AI platforms. Support deployment and integration of Large Language Models (LLMs), Generative AI applications, and AI agents. Collaborate with AI engineers, data scientists, DevOps engineers, and infrastructure teams to deliver production-ready AI environments. Ensure infrastructure security, access management, data protection, and compliance standards. Requirements Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related field. 1-5 years of experience in Cloud Infrastructure, DevOps, Platform Engineering, Infrastructure Engineering, or a related technical field. Strong experience with Linux administration and infrastructure management. Hands-on experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud. Practical experience with Docker and Kubernetes for containerization and orchestration. Understanding of AI/ML infrastructure requirements, including GPU computing and distributed workloads. Experience with GPU technologies, preferably NVIDIA GPUs and CUDA. Knowledge of MLOps practices and tools for model deployment, monitoring, and lifecycle management. Experience with CI/CD pipelines and Infrastructure as Code (IaC) tools such as Terraform or Ansible. Familiarity with model serving and inference frameworks such as NVIDIA Triton Inference Server, vLLM, TorchServe, or KServe. Understanding of networking, storage, scalability, high availability, and infrastructure security. Experience with monitoring and observability tools such as Prometheus, Grafana, or ELK Stack. Strong troubleshooting, analytical, and problem-solving skills. Preferred Qualifications Experience building and managing enterprise AI/ML platforms. Familiarity with MLflow, Kubeflow, KServe, or similar MLOps platforms. Experience deploying and managing LLMs and Generative AI models in production environments. Knowledge of GPU resource scheduling and optimization for AI workloads. Experience with distributed model training, inference optimization, and model quantization. Familiarity with NVIDIA AI Enterprise, NVIDIA NIM, or similar AI infrastructure solutions. Experience with hybrid cloud and on-premises AI infrastructure. Knowledge of security, governance, and access control for enterprise AI platforms. Relevant cloud, Kubernetes, or DevOps certifications are an advantage.