Amisha Singhal

Built and evolved core platform infrastructure by implementing Omnia 1.7 and fine-tuning LoRa and SFT models on NVIDIA H100 GPUs; delivered complete SDLC solutions spanning design, implementation, testing, and operations across Kubernetes fleet environments. Designed lifecycle management automation for containerized applications using Kubernetes, Helm, and Docker; managed cluster lifecycle workflows across distributed multi-node environments reducing provisioning time by 60% and improving workload reliability by 45%. Architected and maintained end-to-end CI/CD pipelines using Jenkins, Docker, Kubernetes, and Helm; introduced standardization and automation behind system build and deployment procedures boosting deployment velocity by 40% and reducing failure rates by 35%. Automated cloud infrastructure provisioning and scaling using Terraform and Ansible on AWS (EC2, EKS, ECS, VPC, IAM); leveraged hyperscale native technologies around storage, networking, and compute to manage consistent, repeatable delivery at scale. Developed Python, Bash, and Shell automation scripts to streamline operational workflows, automate system build procedures, and handle customer service requests in a proactive, analytics-driven manner. Built observability and monitoring pipelines using Prometheus, Grafana, and Splunk for log aggregation, alerting, and real-time performance tracking across Kubernetes microservices; coordinated with expert networks to drive issue resolution reducing MTTR by 30%. Engineered enterprise-grade security integrating AWS IAM policies and security groups; drove technical projects spanning cloud-native infrastructure modernization and ensured compliance across multi-region microservices deployments. Designed a reasoning-driven autonomous platform system with dynamic SOP retrieval and secure orchestration; operated with little day-to-day supervision making technical decisions based on internal conventions and industry best practices across Linux and Kubernetes environments.

Developed ML-driven dashboards using Splunk Enterprise and Machine Learning Toolkit (MLTK), automating data visualization and reducing analysis time by 35%. Deployed machine learning models for clustering, forecasting, and anomaly detection, strengthening operational insights and predictive analytics.

Developed Python and Bash automation scripts to streamline DevOps workflows and increase productivity. Assisted in Kubernetes cluster management, ensuring high availability, efficient resource utilization across distributed environments.