Summary
Deepak Suresh is a Senior Machine Learning Engineer with 6 years of focused AI experience and 11 years in industry, blending research-grade innovation with production deployment. He designs end-to-end ML systems—building datasets, training and evaluating models across images, text, and tabular data, and deploying them with attention to fast inference via distillation and quantization. His work spans LLMs (including a masters thesis that produced a novel sparse LLM inference engine using SuiteSparse/GraphBLAS), GNNs, CNNs, and GANs, and has driven measurable business impact like reduced MTTR for SREs and higher precision in fraud detection. At Infosys he built RAG-based assistants and codebase-documentation tools that accelerated modernization, and his earlier roles delivered sizable time and cost savings for clients. Comfortable across Python, TensorFlow, PyTorch, Keras, Scikit-learn and Julia, he pairs systems-level optimization with practical product outcomes. An uncommon strength is his hands-on ability to translate sparse-matrix research into tangible inference speedups for large language models.
11 years of coding experience
6 years of employment as a software developer
Indian Institute of Technology Madras
Master of Science - MS Computer Science, Master of Science - MS Computer Science at Texas A&M University