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| Python Developer | Linux | AWS |
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
PICSOLICIOUS_ADMIN_PORTAL
May 27, 2026 – Present
PICSOLICIOUS_ADMIN_PORTAL — GitHub repository
View ProjectTechDocs
September 24, 2025 – October 7, 2025
This repo consists of all the notes that I prepared while learning technology.
View ProjectSRE-Learning-Project
September 12, 2025 – September 17, 2025
SRE-Learning-Project — GitHub repository
View ProjectDevOps-Observability
March 25, 2025 – March 25, 2025
DevOps-Observability — GitHub repository
View Projectmylearningnotes.io
September 19, 2024 – September 19, 2024
This repository consists of all the learning notes I prepared for myself.
View ProjectCloudera-Applied-Data-Science-with-Python-Specialization
August 5, 2019 – February 8, 2020
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
View ProjectData_Mining
June 16, 2019 – February 8, 2020
Learning on Classification, Clustering, Regression, Association, Natural Language processing
View ProjectCultural Fit Analysis
The candidate's project portfolio shows a diverse range of interests, including data science, DevOps, SRE, and web development. While this indicates a broad curiosity, it also suggests a lack of deep specialization in the target role of Data Scientist. The majority of projects are personal, which is common for candidates with limited professional experience, but there's no clear indication of collaborative work or alignment with specific organizational cultures. The breadth of technologies (Python, Shell, JavaScript, HCL, Dockerfile) is good, but the depth in data science specific tools beyond basic Python libraries is not evident.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's project descriptions are brief, and there are no completed psychometric or English tests to provide insight into communication or work attitude.