AI/ML Engineer at ADP, USA

Built a multi-agent RAG system using LangGraph and LlamaIndex integrated with AWS Bedrock-hosted Meta LLaMA models to resolve HR payroll and benefits queries faster, reducing escalations to Tier-2 support. Fine-tuned LLaMA models using LoRA/QLoRA on proprietary payroll documentation and compliance datasets to improve domain-specific accuracy and reduce inference cost. Developed FAISS-based semantic retrieval for 500K+ internal HR policy and tax documents, achieving low-latency retrieval. Implemented LightGBM and Prophet/ARIMA forecasting for payroll volume and attrition trends, improving forecast accuracy. Engineered automated ETL pipelines from AWS Redshift into Tableau dashboards to surface real-time payroll anomalies and fraud indicators and reduce manual reporting effort.

Research Assistant – Machine Learning at The University of Texas at Arlington, USA

Designed a hybrid CNN and BERT NLP pipeline in PyTorch for automated classification and NER on 50,000+ unstructured research documents, achieving strong F1 and reducing annotation time. Benchmarked XGBoost against deep learning baselines using structured research data stored in PostgreSQL, improving predictive accuracy and producing a peer-reviewed lab report. Prototyped an evaluation framework using GPT-3.5 Turbo and Claude for automated summarization and entity extraction, benchmarking against NLTK rule systems. Consolidated experimental data into Azure Data Lake, built a centralized warehouse, and created Power BI dashboards to visualize model performance metrics for faculty review.

Junior ML Engineer at Cognizant Technology Solutions, India

Developed churn classification models using Python, Scikit-learn, and SVM for a retail vertical, achieving 87% accuracy and improving retention via targeted campaigns. Implemented model validation frameworks (cross-validation, confusion matrices) across production models to reduce overfitting and improve generalization accuracy. Used SHAP and LIME to explain model predictions to non-technical stakeholders and accelerate deployment approvals. Performed cohort analysis and data wrangling on 1M+ records to standardize inputs and reduce preprocessing time. Built Azure Data Factory ETL pipelines to automate weekly reporting. Built an NLP-based ticket classification module using Keras and NLTK and visualized trends in Power BI to improve routing accuracy.

Data Scientist Intern at IIIT – Hyderabad, India

Analyzed structured and unstructured research datasets using Python, Pandas, NumPy, and SQL; performed data cleaning, feature extraction, and EDA to improve dataset quality. Generated classification and predictive models using Scikit-learn (Logistic Regression, Random Forest) via feature engineering and tuning to improve accuracy. Created visualization and reporting solutions using Matplotlib, Seaborn, and Power BI to help teams monitor KPIs and reduce manual effort.