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AI Engineer with less than a year in Machine Learning & Natural Language Processing
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Aspiring Data Scientist with a strong academic foundation and practical experience in machine learning and AI. Eager to apply analytical and problem-solving skills to develop intelligent, data-driven solutions and contribute meaningfully to real-world innovations.
Fergusson College (Autonomous)
M.Sc. · Data Science
August 1, 2023 – June 30, 2025
Shri Saibaba Senior College, Shirdi
B.Sc. · Computer Science
August 1, 2020 – June 30, 2023
Bhaskaracharya Pratishthana
Machine Learning Engineer Intern
March 1, 2025 – July 1, 2025
Pune, Maharashtra, India
Credit Risk Modeling Using Machine Learning
June 28, 2026 – Present
Credit risk model that predicts the likelihood of a borrower defaulting on a loan, helping financial institutions make informed lending decisions and automate the manual analysis process. Data Sources: Bureau and Internal product datasets. Feature Engineering: Hypothesis Testing (Variance Inflation Factor, ANOVA, Chi-Square). Machine Learning: Decision Trees, Random Forest, XGBoost. Functionality: Improved prediction accuracy and informed lending decisions. Performance: Achieved 88% accuracy and 0.82 ROC-AUC on test data.
Movie Recommendation System
June 28, 2026 – Present
A movie recommendation system is a tool that suggests movies to users based on their preferences. Data Sources: TMDB (The Movie Database) Integrated TMDB API. NLP Techniques: TF-IDF Vectorization, Cosine Similarity. Functionality: Users select a movie and receive top 10 similar recommendations with posters. Performance: Achieved 90% accuracy (based on user feedback and similarity threshold tuning).
YouTube Video Content Analysis ChatBot
June 28, 2026 – Present
Developed an intelligent chatbot system that analyzes YouTube video content through automated transcript processing and natural language understanding. Data Sources: YouTube Transcript API. Framework: Langchain, Streamlit. Technologies: Python, Hugging Face Transformer. Functionality: The system enables users to ask questions about video content and receive accurate, context-aware responses.
Cultural Fit Analysis
The candidate's academic projects demonstrate initiative and a problem-solving mindset, which are positive indicators for cultural fit. The diversity of projects (credit risk, chatbot, recommendation system, OCR) shows a broad interest in applying AI/ML across different domains. The target role of 'AI Engineer' aligns well with the candidate's demonstrated skills and project focus, suggesting a good fit for an innovation-driven environment. However, the lack of team-based project descriptions or collaborative experiences makes it hard to assess collaboration and interpersonal skills.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex problems and deliver functional solutions. The internship experience suggests a capacity for structured development and integration of advanced techniques. However, without specific behavioral or teamwork assessments, it's difficult to fully gauge soft skills and operational fit beyond technical contributions.