From models to operating paths
Notes on retrieval, evaluation loops, serving reliability, and data quality around AI products.
About the author
Systems thinking across AI, data, and BigData.
I write about the practical side of building data-heavy systems: how data moves, where distributed jobs break, how platforms stay observable, and how AI systems become reliable enough to run in production.
What this site is for.
Built at Scale is a notebook for technical judgment: architecture decisions, postmortem patterns, old AWS BigData lessons that still matter, and new AI/data platform design notes that benefit from being written down clearly.
Movement, shape, and trust
Design choices for lakes, streams, transformations, lineage, and platform ergonomics.
Distributed lessons with scars
EMR, Hadoop, Spark, DynamoDB, Kinesis, Elasticsearch, and the operational edge cases around them.
Profile in brief.
Raja Jaya Chandra Mannem has worked across cloud data engineering, distributed systems, platform reliability, and machine-learning-adjacent data workflows. The common thread is translating messy operational constraints into systems that people can reason about.
The older archive captures hands-on AWS BigData work from the EMR, Kinesis, DynamoDB, ElastiCache, Data Pipeline, HBase, Hive, S3, and Elasticsearch era. The newer direction extends that foundation into AI platform thinking: data contracts, observability, batch-to-serving boundaries, evaluation pipelines, and the reliability culture needed for model-backed systems.