Vaishnavi Dalal
Pune, India
Vaishnavi Dalal
Software Developer | AI & Full-Stack Developer
Category : Artificial intelligence (AI)
I am a Software and AI Developer with experience in Python, C++, full-stack development, backend development, and AI-powered applications. I have worked on automation, log analysis, Linux-based systems, and machine learning projects through internships and technical projects.
I specialize in building practical and scalable solutions using Python, FastAPI, React, Next.js, Node.js, databases, Docker, and modern AI technologies such as LLMs and RAG. I can help with web applications, backend APIs, AI integrations, automation, debugging, and custom software solutions.
I am committed to writing clean, reliable code and delivering solutions that meet client requirements. Let's work together to turn your ideas into functional products!
I specialize in building practical and scalable solutions using Python, FastAPI, React, Next.js, Node.js, databases, Docker, and modern AI technologies such as LLMs and RAG. I can help with web applications, backend APIs, AI integrations, automation, debugging, and custom software solutions.
I am committed to writing clean, reliable code and delivering solutions that meet client requirements. Let's work together to turn your ideas into functional products!
Working hours
- Monday:08h00 To 18h00
- Tuesday:08h00 To 18h00
- Wednesday:08h00 To 18h00
- Thursday:08h00 To 18h00
- Friday:08h00 To 18h00
- Saturday:Not available
- Sunday:Not available
• Contributed to reducing Linux Cloud Agent diagnosis time by 82% (from 30 to 5 minutes) by developing an auto
mated Health Check Tool that performs 35+ installation, connectivity, TLS, configuration, and module validation
checks.
• Built an AI-powered log analyzer that processed 10,000+ Cloud Agent log entries, reducing analysis time by 87%
(2 hours to under 16 minutes) and saving 15+ engineering hours weekly through intelligent root cause analysis.
• Resolved 15+ application bugs by investigating root causes, implementing fixes, and validating solutions across
Linux Cloud Agent components.
• Created troubleshooting workflows for 5+ Linux Cloud Agent modules (VM, Policy Compliance, SWCA, FIM,
and EDR), automating 35+ diagnostic checks, reducing manual troubleshooting effort by 80%, and accelerating
issue resolution for engineering and support teams.
mated Health Check Tool that performs 35+ installation, connectivity, TLS, configuration, and module validation
checks.
• Built an AI-powered log analyzer that processed 10,000+ Cloud Agent log entries, reducing analysis time by 87%
(2 hours to under 16 minutes) and saving 15+ engineering hours weekly through intelligent root cause analysis.
• Resolved 15+ application bugs by investigating root causes, implementing fixes, and validating solutions across
Linux Cloud Agent components.
• Created troubleshooting workflows for 5+ Linux Cloud Agent modules (VM, Policy Compliance, SWCA, FIM,
and EDR), automating 35+ diagnostic checks, reducing manual troubleshooting effort by 80%, and accelerating
issue resolution for engineering and support teams.
• Conducted research on urban heat stress analysis and Local Climate Zone (LCZ) classification for the CME using
4 geospatial datasets : IMD weather, MODIS & Landsat imagery, and thermal drone data.
• Processed 20+ years of climate and geospatial data in Google Earth Engine to derive 5+ spatial features, including
Land Surface Temperature (LST), NDVI, NDBI, vegetation density, and LULC, enabling heat stress assessment and
temperature trend analysis.
• Used Random Forest, SVM, and ANN models for LCZ classification, achieving 83% validation accuracy and
forecasting future campus temperature trends through 2050.
• Validated models using ANOVA test and thermal drone observations, automating geospatial preprocessing to reduce
manual analysis by 40% while generating heatmaps, confusion matrices, dashboards, and executive reports.
4 geospatial datasets : IMD weather, MODIS & Landsat imagery, and thermal drone data.
• Processed 20+ years of climate and geospatial data in Google Earth Engine to derive 5+ spatial features, including
Land Surface Temperature (LST), NDVI, NDBI, vegetation density, and LULC, enabling heat stress assessment and
temperature trend analysis.
• Used Random Forest, SVM, and ANN models for LCZ classification, achieving 83% validation accuracy and
forecasting future campus temperature trends through 2050.
• Validated models using ANOVA test and thermal drone observations, automating geospatial preprocessing to reduce
manual analysis by 40% while generating heatmaps, confusion matrices, dashboards, and executive reports.
Please sign in as a customer to give your feedback
Need a service ?
Do you want to hire the services of this Professional ?
Post your need and you will receive dozens of proposals from the Professionals of the community.
