Help & Supportcareers@gravityer.com
AI Engineer with less than a year in Machine Learning & NLP
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Science enthusiast with hands-on experience in building end-to-end Machine Learning and AI systems using Python, NLP, and Large Language Models (LLMs). Skilled in developing RAG-based applications and deploying ML models using FastAPI and Docker. Passionate about solving real-world business problems through data-driven approaches and continuously improving through practical learning.
Brainware University, Kolkata
B.Tech · Computer Science Engineering (Data Science Specialization)
August 1, 2022 – June 30, 2026
R.B.S. Inter College, Patna
Intermediate · Science (PCM)
June 1, 2019 – May 31, 2021
St. Albert's High School, Patna
Matriculation · ICSE
June 1, 2019 – May 31, 2019
Techno Exponent
Industry Internship – Gemstone Price Prediction
August 1, 2025 – November 1, 2025
India
SMS / Email Spam Classifier
January 1, 2026 – Present
Developed an NLP spam detection model on 5,500+ labeled SMS/Email messages using tokenization, stopword removal, stemming, and TF-IDF vectorization. Trained and compared Multinomial Naive Bayes, Logistic Regression, Random Forest, and XGBoost; built a soft Voting Classifier ensemble achieving 97.8% accuracy and 100% precision on spam detection. Deployed as a real-time interactive Streamlit web application, allowing instant message classification with sub-second inference.
View ProjectInsurance Premium Prediction API
January 1, 2026 – Present
Built a FastAPI REST API to classify insurance premium risk (High / Medium / Low) using a Random Forest classifier trained on a custom dataset with features including age, BMI, income, smoking status, city, and occupation. Achieved 75% classification accuracy on the test set; API returns per-class confidence scores enabling downstream threshold-based business logic. Containerized with Docker for reproducible deployment; added Pydantic request validation and auto-generated Swagger / Redoc API documentation.
View ProjectRAG-Powered Text-to-SQL
January 1, 2026 – Present
Built an end-to-end natural language to SQL pipeline using RAG architecture over a MySQL database of 7 tables and 2,277 rows, enabling business users to query data without writing SQL. Implemented schema-aware SQL query generation using LangChain and executed queries using SQLAlchemy. Added a post-query RAG layer to convert raw SQL results into human-readable business answers; integrated RAGAS evaluation framework to measure faithfulness, answer relevancy, and context precision.
View ProjectProgramming for Everybody (Getting Started with Python)
University of Michigan / Coursera
June 1, 2026 – Present
Python Data Structures
University of Michigan / Coursera
June 1, 2026 – Present
Getting Started with Artificial Intelligence
IBM SkillBuild
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in diverse AI/ML applications, from NLP spam detection to RAG-powered text-to-SQL and insurance premium prediction. This breadth of interest aligns well with a dynamic AI engineering role that requires adaptability and a willingness to tackle varied challenges. The focus on end-to-end solutions and deployment aspects also indicates a practical, results-oriented approach. However, the candidate is still pursuing their bachelor's degree, which might imply a need for more structured mentorship and guidance in a professional setting.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving orientation and a drive to build practical applications. The use of Docker and FastAPI suggests an understanding of operationalizing ML models. Participation in hackathons and bootcamps points to a collaborative and continuous learning mindset. However, without direct interview data, assessing communication clarity in real-time, stress handling, or team collaboration remains speculative.