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AI Engineer with less than a year in Generative AI, LLMs, and Machine Learning.
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Data Science and AI aspirant with hands-on experience in PyTorch, Generative AI, and LLM-based systems, focused on building scalable machine learning solutions. Experienced in computer vision, Retrieval-Augmented Generation (RAG), and multi-agent AI systems, with experience designing end-to-end ML pipelines across diverse domains.
Indian Institute of Technology Guwahati
Bachelors of Technology · Mathematics and Computing
August 1, 2022 – June 30, 2026
Multi-Agent AI Travel Planning System using LLMs
October 1, 2025 – December 1, 2025
Designed an end-to-end multi-agent GenAI system that autonomously selects destinations, performs city research, generates day-wise itineraries, and optimizes travel budgets using LLaMA-3.1 models. Implemented CrewAI-based agent orchestration and an alternative LangChain prompt-chaining pipeline to compare agent-driven reasoning with sequential LLM workflows. Built an interactive Streamlit application to capture user preferences (budget, visa, duration, interests) and generate structured markdown-based AI travel plans.
View ProjectContext-Aware Video Intelligence System using Retrieval-Augmented Generation (RAG)
May 1, 2025 – July 1, 2025
Built a Video-based RAG pipeline enabling grounded question answering and summarization over local and YouTube videos. Applied semantic chunking and FAISS vector search to retrieve timestamped, contextually relevant video segments. Enforced LangChain-based prompt constraints to minimize hallucinations and ensure context-only responses.
View ProjectBilingual Neural Machine Translation (NLP) with Attention
February 1, 2025 – April 1, 2025
Developed an end-to-end NLP system for English Hindi Neural Machine Translation using a Seq2Seq LSTM Encoder-Decoder with Luong Attention. Preprocessed and curated a 160K+ sentence bilingual corpus using tokenization, stop-word removal, 20K+ vocabulary construction, and sequence padding for scalable training. Applied GloVe embeddings and attention mechanisms, improving translation quality with a BLEU score increase from 0.87 to 0.97 and achieving 99.79% training accuracy.
View ProjectAI-Driven Classification of Astronomical Objects
July 1, 2024 – July 1, 2024
Processed the SDSS dataset using preprocessing and exploratory analysis, including univariate and multivariate visualizations, boxplots, class distributions, and PCA for Stars, Galaxies, and QSOs. Built ML pipelines using KNN, Random Forest, and XGBoost with GridSearchCV-based hyperparameter tuning, achieving 99.27% accuracy and an F1-score of 0.9922.
View ProjectCultural Fit Analysis
The candidate demonstrates a strong cultural fit for an AI Engineer role through a diverse portfolio of personal projects directly aligned with modern AI/ML trends (GenAI, LLMs, RAG, NLP). Their involvement in extracurricular activities like open-source contributions and coding clubs indicates a collaborative spirit and a passion for technology, which are valuable traits in a dynamic tech environment. The breadth of skills and project types suggests adaptability and a continuous learning mindset.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and self-driven approach to learning and applying advanced AI concepts. Participation in coding clubs and open-source contributions suggests a collaborative mindset and a willingness to engage with technical communities. The detailed project descriptions also imply good problem-solving skills and an ability to articulate technical solutions.