AI/ML Engineer - NLP & Generative AI

Ram Narayan Jaiswal

AI/ML Engineer Data Science

******************@*****.*** 956-***-**** Bengaluru LinkedIn GitHub Professional Summary

AI/ML Engineer with hands-on experience in Machine Learning, Deep Learning, NLP, Computer Vision, and Generative AI. Skilled in Python, PyTorch, LangChain, FastAPI, and RAG, with experience developing end-to-end AI solutions, ML pipelines, and scalable applications. Experience

Data Science Intern

Codec Technologies

02/2026 – 05/2026

•Performed data preprocessing, exploratory data

analysis (EDA), and feature engineering on

structured and unstructured datasets.

•Developed NLP-based classification models using

BERT/Transformers and LSTM architectures.

•Built computer vision pipelines using CNN, ResNet, and DenseNet for image classification.

•Conducted model evaluation, hyperparameter tuning, and cross-validation to improve model performance.

•Improved model detection accuracy by 15% through

systematic tuning and evaluation.

Technical Skills

AI/ML & NLP

Machine Learning, Deep Learning, NLP, Computer Vision, LLMs, RAG, Agentic AI, PyTorch, Hugging Face

Frameworks & Libraries

LangChain, Scikit-learn, OpenCV, NumPy, Pandas

Backend & Tools

FastAPI, Streamlit, REST APIs, SQL, Git

Languages

Python, C++, SQL

Core CS

Data Structures & Algorithms (DSA), OOP, Model

Optimization

Education

B.Tech, CSE (AI)

Dr. A. P. J. Abdul Kalam Technical University, Uttar Pradesh, Lucknow

2022 – 2026 Greater Noida

CGPA : 7.44/10

Projects

Smart Research Assistant (Agentic AI & LLMs)

Python, FastAPI, Streamlit, LangChain, LLMs

•Built an AI-powered assistant using LangChain and FastAPI to summarize large documents (200MB) via

map-reduce methodologies.

•Implemented RAG-based Q&A by integrating

OpenRouter API, achieving a 90%+ comprehension

accuracy rate.

•Designed a responsive frontend using Streamlit for real-time user interaction with LLMs.

Message Intelligence Platform

Python, Streamlit, NLP, TF-IDF, Information Retrieval

•Built a message intelligence pipeline for classification, task/event extraction, priority scoring, and routing.

•Implemented semantic search using TF-IDF and

cosine similarity across 1,080 messages.

•Developed priority and message-grouping engines

using urgency signals, similarity, and entity

extraction.

•Optimized retrieval to achieve <5ms query latency using pre-built TF-IDF indexes.

ECG Arrhythmia Detection Model

Python, Deep Learning, Scikit-learn

•Developed a Deep Learning pipeline to classify

cardiac rhythms from ECG signals.

•Applied feature extraction and preprocessing

techniques for arrhythmia classification.

•Achieved 95%+ classification accuracy on the ECG

arrhythmia dataset.

End-to-End Heart Disease Predictor

Python, Logistic Regression, Random Forest

•Trained predictive models (Logistic Regression,

Random Forest) on clinical data and deployed a

scalable web application.

Certifications & Achievements

ML, Machine Learning – NPTEL

Successfully completed an 8-week NPTEL course

covering fundamental Machine Learning concepts and algorithms.

AI/ML Training Program — IIT Kanpur

100 Hours Feb 2025 – Apr 2025

Technical Training

Completed professional tracks in Python and C++

Programming via Coding Blocks and Udemy.

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