Summary
Srinivas Kulkarni is a Machine Learning Engineer with 9 years of experience building and deploying deep learning solutions across cloud platforms, with hands-on roles at McKinsey, Quantiphi, and research at UT Dallas. He blends signal-processing expertise from his master's in DSP with practical ML engineering—designing CNNs for speech/noise classification, optimizing architectures to 91%+ accuracy, and exporting models to mobile. At Quantiphi he led end-to-end ML workflows from hyperparameter search and incremental learning to serverless inference on AWS and codebase refactoring for production deployment. His background in embedded systems testing gives him a pragmatic edge in reliable model integration and debugging across constrained environments. GCP and AWS certified and comfortable advising clients on trade-offs, he translates quantitative model outputs into actionable business recommendations. Based in Marlborough, MA, he combines academic rigor with client-facing consulting experience, often bridging research innovations into production features.
9 years of coding experience
5 years of employment as a software developer
Master’s Degree, Digital Signal Processing, Master’s Degree, Digital Signal Processing at The University of Texas at Dallas
Bachelor’s Degree, Electrical, Electronics and Communications Engineering, A, Bachelor’s Degree, Electrical, Electronics and Communications Engineering, A at Sri Jayachamrajendra College Of Engineering, Mysore
English, Kannada, Hindi