Hello!
I'm Sakshi Patel, an aspiring GenAI Engineer and Data Specialist, building intelligent, data-driven systems that automate complex processes with innovative technologies.
Get in touch sakshipatel2598@gmail.com
Recent graduate with a Master's in Information Technology and Managementfrom The University of Texas at Dallas. I have a strong background in data engineering and generative AI, with a passion for building scalable, data-driven systems
My recent experience as a GenAI Engineer Intern at Insight Global involved developing an AI- powered Statement of Work generator using LangGraph and a multi-agent approach.I also created a Retrieval - Augmented Generation(RAG) system that improved document accuracy
Previously, I worked as a Data Analyst at LTIMindtree, where I engineered and automated data pipelines for over 100 million records, significantly reducing system downtime.I also developed Tableau dashboards that improved reporting efficiency and leveraged skills in Python, SQL, and ETL tools.
- Python
- JavaScript
- Typescript
- SQL
- Java
- R
- React.js
- Node.js
- LangGraph
- RAG
- GraphQL
- Git
- MongoDB
- PostgreSQL
- Snowflake
- AWS
- Azure
- Seeburger BIS
- Tableau
- Power BI
- Grafana
- NLP
- Feature Engineering
- Statistical Modeling
- Developed an AI-powered SOW generator using LangGraph with multi-agent orchestration, automating drafting, validation, and feedback loops to reduce manual effort by 30%.
- Built a Retrieval-Augmented Generation (RAG) system leveraging LLMs and NLP-driven automation, improving document accuracy by 25% and incorporating vector embeddings stored in PostgreSQL (pgvector).
- Implemented scalable user modules with React/TypeScript and Redux Toolkit, implementing features like template customization, real-time collaboration, and document preview that enhanced user workflow efficiency.
- Created automated ETL pipelines using Python and SQL to extract, clean, and transform large-scale driver training data, enhancing data integrity and reducing processing time by 35%.
- Deployed the project on Azure, utilizing Azure Compute for scalable processing and Azure PostgreSQL for secure, high-performance data storage alongside integration with Azure OpenAI for LLM inference.
- Mentored 50+ students on AWS, Hadoop/Spark, Tableau, Power BI, and Python for real-world data analytics projects.
- Developed curriculum and provided feedback to enhance analytical skills
- Engineered SQL-based automation and data integration scripts for 100M+ transaction records with 99.99% uptime and 94% transformation accuracy.
- Optimized data pipelines using Seeburger BIS and Python by introducing automation scripts and validation logic, decreasing system downtime by 30% and reducing manual interventions by 50%.
- Devised Tableau dashboards for operations and finance teams, driving a 20% increase in reporting efficiency and 15% faster decision-making cycles.
- Created Power BI and Tableau dashboards highlighting user retention and growth, increasing consultation-based revenue by 25%.
- Developed SQL queries and AWS Athena pipelines for real-time metrics and program performance analysis.
- Automated Excel-based stakeholder reports, reducing repetitive tasks by 50%
- Built and optimized SQL pipelines to extract and transform REST API data for business reporting, decreasing query response time by 40%.
- Designed and deployed Tableau dashboards for automated insights, reducing manual reporting by 20%.
Built Hadoop/Spark infrastructure to process 10M+ records, reducing latency by 30%. And created React-based dashboard integrated with Tableau to track driver behavior and incident risk zones, reducing fleet accidents by 25%.
Analyzed 3M+ grocery transactions with Python & SQL, improving cross-sell strategy by 15% & forecast accuracy by 25%. And Visualized key insights using Tableau dashboards to support strategic planning and boost retention by 18%.
Integrated an internal knowledge management chatbot using LangChain and OpenAI, enabling employees to retrieve insights from 1,000+ documents with 85% retrieval accuracy.