Junior AI/ML Engineer Intern with less than a year in Machine Learning & NLP Systems
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Junior AI/ML Engineer (Entry-Level) with hands-on experience in building, fine-tuning, and deploying machine learning and NLP systems. Strong foundation in Python, PyTorch, and transformer-based models with practical exposure to end-to-end ML pipelines — including RAG, vector search, LLM APIs, and MLOps. Experience delivering production-ready deployments through academic projects and internship-level industry work.
University of Rajasthan
Masters of Computer Application
August 1, 2023 – June 30, 2025
Mohanlal Sukhadia University
Bachelor of Science
August 1, 2019 – June 30, 2022
Maxgen Technologies Pvt Ltd
AI/ML Intern
May 1, 2025 – October 1, 2025
India
DebateRAG | Multi-AgentSystem
June 24, 2026 – Present
Built a multi-agent research debate system where autonomous agents argue opposing positions on a topic using semantically retrieved evidence from a vector knowledge base. Designed a RAG pipeline using BGE-large-en-v1.5 embeddings and ChromaDB (cosine similarity) with custom chunking strategies to preserve argumentative context across document boundaries. Orchestrated agent interactions using LangGraph state machines with a FastAPI backend and Streamlit interface for real-time debate visualization.
View ProjectLegal Document Classifier | NLP Application
June 24, 2026 – Present
Fine-tuned nlpaueb/legal-bert-base-uncased on 35,000 samples across 7 LexGLUE datasets (SCOTUS, ECtHR, EUR-Lex, LEDGAR, CaseHOLD, UnfairToS) achieving 85% accuracy and 84.66% macro F1 across 7 document categories. Built a stratified data pipeline balancing 7 heterogeneous legal datasets with MLflow tracking, fp16 training, and early stopping for reproducibility.
View ProjectCultural Fit Analysis
The candidate's academic projects and internship show a strong interest and hands-on experience in cutting-edge AI/ML areas like multi-agent systems, RAG, and legal NLP. This indicates a good fit for an innovative and research-oriented environment. The diversity of projects (RAG, NLP classification, stock prediction) and exposure to various tools (LangGraph, MLflow, FastAPI, Streamlit) suggest adaptability and a willingness to explore different problem domains, which is beneficial for cultural fit in a dynamic team. The target role of 'Junior AI/ML Engineer Intern' aligns well with their current experience level and demonstrated skills.
Soft Skills & Operational Fit
The candidate demonstrates a proactive approach to learning and applying advanced AI/ML concepts. Their project work indicates an ability to work independently on complex technical challenges. The internship experience suggests an understanding of practical deployment and optimization, which aligns with operational needs. However, without specific behavioral or situational assessment data, a deeper evaluation of soft skills like teamwork, problem-solving under pressure, and communication in a team setting is not possible.