Headline

Python Developer | Data Science | Data Analytics | AI & Machine Learning | Turning Data into Intelligent Solutions

About

Computer Science and Engineering graduate specializing in Generative AI and LLM-integrated systems. Experienced in building RAG pipelines, AI agents, and prompt engineering workflows, with working knowledge of LangChain, Hugging Face, and OpenAI APIs. Skilled in REST APIs, backend services, and vector-database-backed applications, with foun- dations in OOP, DSA, and Scikit-learn-based ML. Strong analytical and problem-solving skills, collaborative and quick to learn in fast-paced environments. Technical Skills Languages: Python, SQL, TypeScript, JavaScript Generative AI / LLM: LLMs, RAG, Prompt Engineering, AI Agents, AI Chatbots, LangChain, Hugging Face Transformers, OpenAI API, Google Gemini, Google ADK AI/ML: Machine Learning, Scikit-learn, Vector Embeddings, Vector Database (ChromaDB) Backend/APIs: REST APIs, Flask, Express.js, JSON, Microservices Databases: SQL/NoSQL, PostgreSQL, MySQL, Firebase, Supabase Concepts: OOP, Data Structures & Algorithms, SDLC, DBMS Cloud & Tools: AWS EC2 (production deployment), basic familiarity with Azure/GCP, Git, GitHub, GitHub Actions, Docker Soft Skills: Analytical & Problem-Solving Skills, Communication, Teamwork, Eagerness to Learn

Location

Bengaluru

AI stack

antigravity

claude code

deepseek

grok

hugging face

langchain

Work experience

Intern at MindMatrixEd, Dec 2025 - May 2026 · 6 mos

Intern at Elevate Labs, Nov 2025 - Dec 2025 · 2 mos, Remote

Intern at InLighnX Global Pvt Ltd (InLighn Tech), Sep 2025 - Oct 2025 · 2 mos, Bengaluru, Karnataka, India, Completed a one-month internship focused on real-world data science and machine learning applications. • Heart Disease Prediction: Built an ML model using the UCI dataset; performed data preprocessing, feature selection, and trained models (Logistic Regression, Decision Tree, Random Forest, SVM). Achieved ~85% accuracy with Random Forest after hyperparameter tuning. • Visualized feature importance (cholesterol, chest pain, etc.) and model performance using Matplotlib/Seaborn. • Application Development: Designed and deployed a Flask/Streamlit-based web app for real-time heart disease prediction, tested for accuracy, precision, and recall. • Fake News Detection: Implemented an NLP + LSTM-based model to classify news as real/fake; applied text preprocessing, tokenization, embeddings (TF-IDF, Word2Vec, GloVe), and sequence padding. • Gained hands-on experience in Python, Scikit-learn, Pandas, Flask, Streamlit, NLP, and GitHub while working on end-to-end project development. Key Skills: Data Science · Machine Learning · Deep Learning · NLP · Python · Flask · Streamlit · GitHub

Education

Bachelor of Engineering - BE, Computer Science, HKBK Group of Institutions

Public links

github: https://github.com/eshrathaziz