Kamal Sharma

- Deployed 5+ production-grade SaaS applications for global clients, improving client operational efficiency by up to 40 - Implemented end-to-end CI/CD pipelines that reduced deployment time from days to under 2 hours, enabling faster feature releases. - Containerized 10+ microservices using Docker, resulting in 60% improvement in application scalability and deployment consistency. - Provisioned and managed cloud infrastructure on AWS using Terraform, reducing infrastructure provisioning time by 70%.

- Built and benchmarked 4–5 ML models across supervised learning, NLP, and deep learning domains using scikit-learn and pandas, covering regression, decision tree classifiers, text classification pipelines, and neural network architectures. - Designed reproducible preprocessing pipelines with normalisation, stratified train/test splitting, and structured experiment documentation — making all model iterations auditable and repeatable across the team.