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AI Engineer with less than a year in Python Backend Development & ML Model Training
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Final-year B.Tech CSE student with hands-on experience in Python backend development, ML model training, and REST API integration. Built 3 end-to-end projects using Flask, Scikit-learn, and Google Gemini API. Targeting backend/ML engineering roles.
Lovely Professional University
B.Tech · Computer Science and Engineering
August 1, 2022 – June 30, 2026
St. Xavier's High School
12th · Science
N/A – May 31, 2022
St. Xavier's High School
10th · Science
N/A – May 31, 2020
Healthcare Prediction and Assistance Platform
January 1, 2026 – April 1, 2026
Built Flask REST API backend integrating Google Gemini API to process natural language symptom inputs and return structured disease predictions. Deployed AI chatbot using Python and Gemini API for conversational symptom collection, improving downstream prediction data quality and reducing incomplete inputs. Engineered end-to-end data pipelines with Pandas and NumPy to ingest, clean, and preprocess healthcare datasets for AI-driven diagnostic inference.
RESTful Booker API Testing Project
December 1, 2025 – December 1, 2025
Designed and executed 50+ REST API test cases across full CRUD booking lifecycle, validating HTTP status codes, headers, and JSON payloads via functional, regression, and negative testing. Automated API flows using Postman JavaScript pre/post-request scripts to dynamically generate auth tokens, chain dependent requests, and validate response schemas. Built reusable Postman collections with environment variables across dev and test environments, improving regression test repeatability and reducing manual setup time.
Language Detection System
October 1, 2025 – November 1, 2025
Trained a multi-class Scikit-learn classifier to detect 30+ languages using TF-IDF feature extraction and supervised learning on a large labeled CSV dataset. Built full NLP preprocessing pipeline using NLTK and Pandas — tokenization, stopword removal, TF-IDF vectorization — boosting model input quality and classification accuracy. Executed complete ML workflow in Jupyter Notebook: data ingestion, cleaning, feature engineering, model training, and evaluation — delivering a production-ready language detection system.
Machine - Learning With Python
By IBM
September 1, 2025 – Present
Data Structures and Algorithms
By Geeks for Geeks
August 1, 2024 – Present
Generative AI with Large Language Models
aws
June 1, 2024 – Present
ChatGPT Advanced Data Analysis
Vanderbilt University
May 1, 2024 – Present
Introduction to Large Language Models
Google Cloud
February 1, 2024 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a breadth of skills across AI/ML, backend development, and API testing. The projects are diverse enough to show adaptability and a willingness to learn different aspects of software development. The certifications in Generative AI and LLMs align well with an AI Engineer role, indicating a strong interest and self-driven learning. However, the lack of professional experience limits the assessment of cultural fit in a corporate environment.
Soft Skills & Operational Fit
The candidate's project descriptions suggest an ability to work on structured tasks and follow a complete ML workflow. The focus on improving data quality and test repeatability indicates an attention to detail and operational efficiency. However, without direct work experience or psychometric test results, it's difficult to assess stress handling, team collaboration, or broader work attitude.