• Designed and maintained scalable data pipelines using Python, SQL, and cloud storage, processing semi-structured logs

and metrics to support analytics, reporting, and performance investigations across engineering workflows.

• Analyzed infrastructure workload behavior by profiling CPU, memory, and IO patterns in Linux environments; built

lightweight scripts to compare runs and identify bottlenecks impacting throughput and latency.

• Delivered production-quality code with git-based CI/CD, containerized services using Docker and Kubernetes, and

collaborated cross-functionally to automate ingestion, validation, and continuous improvement of data and analysis

Graduate Teaching Assistant for CSEN 275-Object Oriented Analysis, Design and Programming

Graduate Teaching Assistant for CSEN 359-Object Oriented Design Pattern

• Engineered GPU-aware ETL pipelines using Apache Spark and Databricks, applying partition pruning, join optimization, and

memory-efficient transformations across petabyte-scale analytics workloads on distributed clusters.

• Optimized compute and storage I/O paths via vectorized query execution and Parquet-level compression, improving query

throughput by 38% and memory utilization across multi-node systems.

• Integrated CUDA-enabled data preprocessing modules with Python APIs, accelerating aggregation and filtering tasks using RAPIDS

cuDF, and benchmarking kernel performance for high-density datasets.

• Built an AI-driven ETL pipeline for twitter sentiment analysis using LSTM models deployed on Azure Cognitive

Services, improving accuracy by 30% through advanced preprocessing.

• Used LSTM networks on scraped data for context-aware twitter sentiment predictions, utilizing Python libraries

for efficient data preprocessing and achieving real-time sentiment scoring with Azure ML.

• Developed real-time twitter sentiment dashboards using Azure Power BI, integrating LSTM model outputs with

automated pipelines via Azure DevOps and ARM templates, reducing manual analysis time by 50%

• Network Performance Optimization: Implemented TCP/IP networking protocols for efficient data

transmission and real-time communication between IoT devices and cloud servers, improving data reliability

• System Virtualization & Cloud Integration: Used Docker to containerize IoT applications for seamless

deployment, reducing resource utilization by 20%, and integrated devices with cloud platforms to achieve

99.8% data transmission reliability.

• Real-Time Traffic Monitoring: Enhanced network reliability by deploying tools like Wireshark to monitor

traffic flow and optimize routing protocols, reducing latency in IoT communication by 15%.