Akshansh is a Research Engineer and data & AI platform specialist with 7+ years building production-grade, real-time ML and multimodal data systems and a total of 11 years in engineering roles. He designs high-throughput streaming and lakehouse platforms that make training, post-training evaluation, and inference reproducible, auditable, and production-ready, with hands-on expertise in Kafka, Flink, Spark, Databricks/Delta, and cloud-native tooling. His work has powered edge-to-cloud robotics pipelines for 500+ devices, terabyte-scale LLM training datasets, and healthcare-grade streaming platforms with 99.9% uptime—demonstrating an uncommon blend of applied research and service-ready infrastructure. At Pareto he focuses on human-in-the-loop systems and training-data foundations, translating experimental research into deployable platforms with strong governance and observability. He holds an MS in Computer Science from USC and brings a background in spatial-visual indexing and crowdsourcing systems that informs his pragmatic approach to messy, multimodal data.
11 years of coding experience
8 years of employment as a software developer
Master's degree Computer Science, Master's degree Computer Science at University of Southern California
Bachelor of Technology (B.Tech.) Information Technology, Bachelor of Technology (B.Tech.) Information Technology at Maharaja Agrasen Institute Of Technology, Delhi
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