Hari Kumar Bugide

Background

About Hari Kumar Bugide

I work on (and have worked on)- AI engineering & applied machine learning (ML/DL, NLP/Text Mining)- LLM integration for real use cases (summarization, document insight extraction)- MLOps: CI/CD, orchestration, monitoring, governance on Azure, AWS, and GCP - Data engineering with Spark/Hive, Hadoop/HDP, Databricks; lakehouse & warehouses (Snowflake, Synapse, Redshift)- Statistical modeling, multivariate analysis, and solutions for supervised & unsupervised problems - Scalable ETL/ELT pipelines and schema design (star & snowflake)- Python product development: Flask/Django APIs, backend services, Dockerized deployments - Source‑to‑target mapping, data sanitation, validation, and documentation - Advanced SQL (incl. MySQL) for analytics and application workloads - TensorFlow and graphical models for deep learning projects Bio: Hari Kumar Bugide is a data science and engineering professional building end‑to‑end ML products across statistical modeling, deep learning, NLP/text mining, and large‑scale data engineering. Hari designs and deploys automated ML pipelines using modern MLOps practices while meeting security and privacy requirements across Azure, AWS, and GCP. Work spans translating business requirements into analytical models and ranking systems, developing production APIs with Python (Flask/Django), and architecting reliable data workflows on both on‑prem and cloud. Hari has hands‑on experience with big‑data ecosystems (Hadoop/HDP, Spark, Hive), lakehouse/warehouse platforms (Databricks, Snowflake, Azure Synapse, Amazon Redshift), and robust ETL/ELT processes for downstream analytics. Data lineage is documented via source‑to‑target mapping, with quality maintained through sanitation and validation. Background also includes schema design (star/snowflake), advanced SQL, TensorFlow, and integrating LLMs to analyze unstructured text—such as claim documentation analysis, patient‑interaction summarization, and key‑insight extraction.