AI Engineer with less than a year in Machine Learning, Generative AI, and RAG systems.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI/ML Engineer and B.Tech AIML student with hands-on experience in Machine Learning, Generative Al, Al Agents, and Retrieval-Augmented Generation (RAG) systems using Python. Skilled in FastAPI, LangChain, LangGraph, OpenAI APIs, and Vector Databases. Passionate about building Al-powered applications, backend Al systems, and intelligent automation tools.
Sunder Deep Engineering College, AKTU
B.Tech in Computer Science Engineering (AI & ML) · Computer Science Engineering (AI & ML)
August 1, 2022 – June 30, 2026
Bright Career Academy, Ambah
Class 12 (MPBSE)
N/A – May 31, 2022
ML Chatbot
June 5, 2026 – Present
Developed an NLP-based chatbot using TF-IDF vectorization and Logistic Regression. Performed preprocessing including tokenization, stopword removal, and text cleaning.
Weather Data Analysis
June 5, 2026 – Present
Performed exploratory data analysis and visualization on historical weather datasets using Pandas and Matplotlib.
AI PDF RAG Chatbot
June 5, 2026 – Present
Building a Retrieval-Augmented Generation (RAG) chatbot for answering questions from uploaded PDF documents. Using LangChain, FastAPI, OpenAI APIs, and vector databases for semantic search and context-aware responses.
LangGraph AI Agent
June 5, 2026 – Present
Developing an AI agent system with memory, tool calling, and multi-step reasoning workflows using LangGraph. Integrating external APIs and conversational state management for intelligent task execution.
Local LLM Chat Application
June 5, 2026 – Present
Building a local AI chat application using Ollama, FastAPI, and open-source LLMs. Exploring Docker-based deployment for scalable local inference environments.
Student Performance Predictor
June 5, 2026 – Present
Built a Linear Regression model to predict student performance using Python and Scikit-learn.
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest and foundational knowledge in AI/ML, which aligns with an AI Engineer role. The diversity of projects (RAG chatbot, AI agents, local LLMs, traditional ML) shows a broad exploration within the AI domain. However, all projects are academic, and there is no professional experience, which limits the assessment of cultural fit in a corporate setting. The candidate is still pursuing a bachelor's degree, indicating a junior profile.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex technical problems. However, without direct work experience or psychometric test results, it is difficult to assess soft skills like teamwork, communication in a professional setting, or stress handling. The academic nature of all projects suggests a learning-oriented individual, but operational fit in a fast-paced industry environment is unproven.