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Python Engineer with less than a year in Python, Data Science, and Machine Learning.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
MCA student skilled in Python, Data Science, and Machine Learning with hands-on experience building AI applications and data analytics projects using Pandas, SQL, and Power BI. Seeking entry-level Data Analyst or Python Developer role.
Swami Ramanand Teerth university
Master of computer application
September 1, 2024 – June 1, 2026
Tata internship
GenAI powered Data Analytics
June 1, 2026 – Present
India
InAmigos Foundation
Ai Web Developer intern
June 1, 2026 – Present
India
AI voice Assistant
June 1, 2026 – Present
Designed a application using python, tkinter and sql for frontend. Work Seamlessly with different application such as WhatsApp, Twitter and Instagram with 90% accuracy. Save the time which required for typing by 70%.
AI-powered Document Analysis & Extraction
June 1, 2026 – Present
This API extracts, analyzes, and summarizes documents (PDF, DOCX, Images). It uses OCR and NLP techniques to extract structured information.
Meditation app
June 1, 2026 – Present
Made a full meditation app with flutter and dart. The platform used is VS code and android studio.
AICTE training and learning certificate
AICTE
June 1, 2026 – Present
Virtual internship by CDAC
cdac
July 14, 2025 – Present
Cultural Fit Analysis
The candidate's academic projects and internships show an interest in AI, data analytics, and web development, aligning with a Python Engineer role that often involves these areas. The diversity of projects (voice assistant, document analysis, meditation app) indicates a broad interest in applying technology. However, the experience level is entry-level, which might require significant mentorship and ramp-up time in a senior-level environment.
Soft Skills & Operational Fit
The candidate lists 'Excellent Communication' and 'people management' as soft skills. The project descriptions are concise but lack detailed operational context or challenges faced. The academic nature of projects and internships suggests a foundational understanding but limited real-world operational experience.