Akshay

• Developed scalable data processing pipelines using Databricks and PySpark to handle large-scale agricultural and prospect to cash datasets, improving data processing efficiency by 300%.

• Implemented comprehensive MLOps workflows using MLflow for experiment tracking, model versioning, and automated deployment, reducing model deployment time from weeks to hours.

• Built real-time data streaming solutions using PySpark Structured Streaming on Databricks, processing millions of records daily for predictive analytics in agricultural operations.

• Designed and deployed machine learning models for global business insights, leveraging cloud services and automated pipelines for reliable model updates.

• Built the front-end for a large-scale scheduling tool used by over 500 hospital nursing units, using React JavaScript (ES6+) and TypeScript to turn complex data into a clean, easy-to-use interface.

• Tackled a huge performance bottleneck in our main app, cutting a load time that took over a minute down to less than a tenth of a second, a 1000x speedup that made a critical feature usable again.

• Led the effort to modernize our codebase by swapping out an old, slow library across more than 20,000 lines of code, which made the app faster & secure and future development much easier for the whole team.