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Presentation
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!
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Background
Software Engineering Intern
• 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.
Research Intern
• 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.
Bachelor Of Engineering
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Working hours
Monday
From08h00To18h00
Tuesday
From08h00To18h00
Wednesday â–ª Today
From08h00To18h00
Thursday
From08h00To18h00
Friday
From08h00To18h00
Saturday
Closed
Sunday
Closed
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