Arun Rajendran is a Machine Learning Engineer II with 11 years of experience building production-ready ML and NLP systems, currently working at Abnormal AI in Vancouver. He has a strong HPC and applied-math foundation from an MS in Applied Mathematics (UBC), which he leverages to optimize large-scale models using MPI, CUDA and GPU pipelines. Arun's career spans end-to-end ML product delivery—from training BERT/Pegasus/Llama variants for summarization and paraphrasing to deploying microservices on Kubernetes, Docker and AWS with monitoring and MLflow lifecycle management. He has led multimillion-dollar, cross-functional projects that combined NLP, computer vision and knowledge-graph extraction, and has hands-on experience improving production throughput and reducing latency. Notably, his research background in parallel solvers for 3D Navier–Stokes informs a pragmatic focus on performance and numerical robustness in ML systems.
11 years of coding experience
7 years of employment as a software developer
Indian Institute of Technology Madras
MS in Applied Mathematics Fluid Dynamics, MS in Applied Mathematics Fluid Dynamics at The University of British Columbia
Nanodegree Neural Network Foundations, Nanodegree Neural Network Foundations at Udacity
Grade VIII to XII, Grade VIII to XII at Omega International School
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Arun Rajendran - Machine Learning Engineer II at Abnormal AI