Developed and implemented sophisticated web scraping solutions for automated data extraction from complex web applications and enterprise portals.

Designed and deployed end-to-end automation workflows that significantly reduced manual intervention and improved operational efficiency across multiple business units.

Technical Stack: Selenium WebDriver, Beautiful Soup

Design and implementation of sophisticated machine learning solutions for complex business problems, enabling data-driven insights and automated decision-making systems.

Core Expertise: Gradient Boosting (XGBoost, LightGBM, CatBoost), Ensemble Methods (Random Forest), Clustering (K-Means), Dimensionality Reduction (PCA), Statistical Modeling

Technical Stack: scikit-learn, statsmodels, pandas, NumPy

Architected and implemented custom neural networks for Natural Language Processing, enabling automated customer intent classification across multiple communication channels with high accuracy.

Developed and deployed advanced Computer Vision solutions for retail analytics, optimizing inventory management and merchandising efficiency for enterprise-level clients.

Technical Stack: PyTorch, TensorFlow, Keras, Neural Network Architecture Design & Optimization

Extensive experience with Google Cloud Platform (GCP), specializing in ML model deployment using Vertex AI and large-scale data processing with BigQuery.

Cross-platform expertise across major cloud providers (AWS, Azure, GCP), focusing on compute infrastructure optimization and scalable ML deployments.

Core Technologies: Vertex AI, BigQuery, Model Garden, Cloud Storage, Cloud SDK, Dataflow, Looker, EC2

Designed and implemented robust data architectures and ETL pipelines, leveraging dbt Cloud for scalable data transformations and maintaining data quality.

Extensive experience with diverse database technologies, including BigQuery for analytics, MSSQL for transactional data, and MongoDB for document-based storage solutions.

Technical Stack: Google BigQuery, Microsoft SQL Server, dbt Cloud, MongoDB Atlas, SQLite, DBeaver.

Design and implementation of Retrieval-Augmented Generation (RAG) pipelines, integrating domain-specific data for accurate and context-aware AI responses.

Expertise in vector embeddings to efficiently encode, search, and retrieve information using state-of-the-art vectorization techniques.

Technical Stack: LLMs, LangChain, LlamaIndex, ChromaDB, OpenAI APIs.