Amisha Singhal

Enhanced performance and optimized resource utilization by implementing Omnia 1.7 and finetuning LoRA and SFT models on NVIDIA H100 GPUs. Scaled Llama 3 (8B/70B) models across 18 nodes to improve computational efficiency and model accuracy and performed pretraining on the same cluster. Orchestrated NVIDIA Base Command Manager and Kubernetes deployments, reducing cluster provisioning time by 60% and accelerating workload management efficiency by 45%, resulting in improved overall system performance. Deployed OpenShift on NVIDIA control plane nodes, installed GPU, Network, and OpenShift AI Operators, and configured Node Feature Discovery (NFD) to support high-performance workloads. Architected and implemented automated CI/CD pipelines using Docker, Kubernetes, Mini-Kube, and Terraform, enhancing infrastructure management and application deployment. Boosted scalability and accelerated deployment velocity of 40%, enabling faster, more reliable software delivery across environments. Containerized applications with Docker and orchestrated deployment on AWS ECS. Utilized ansible for scalable configuration management and task automation across heterogeneous environments. Built and optimized image build pipelines to enhance performance, ensure security, and maintain compliance. Designed a robust disaster recovery plan leveraging Jenkins for automated backups and terraform for seamless infrastructure replication, cutting recovery time by 60%. Maximized reliability and scalability by ensuring continuous availability. Provisioned scalable AWS infrastructure with Terraform, integrating Prometheus and Grafana for monitoring.

Leveraged Splunk Enterprise and Machine Learning Toolkit (MLTK) to solve ML/DL use cases such as smart clustering, smart forecasting, and smart outlier detection. Built and operationalized machine learning models, creating data visualization dashboards that upgraded decision-making speed by 41.3% and reduced data analysis time by 35.5%.

Designed and implemented Python and Bash automation scripts, enhancing operational efficiency and reducing manual effort. Assisted in Kubernetes cluster management, ensuring high availability and efficient resource utilization.