Anik Chand

Kolkata, India
[email protected] | +91 9153772355
Portfolio | LinkedIn | GitHub | Kaggle

Summary

Aspiring Machine Learning Engineer with proven experience in designing, training, and deploying AI/ML models using TensorFlow, Scikit-learn, and FastAPI.
Proficient in data preprocessing, statistical analysis, data analysis, data science, and end-to-end model deployment.
Hands-on expertise in full-stack application development and strong API integration.
Recognized for mentorship, problem-solving, and success in coding competitions, with a strong passion for building scalable AI solutions.

Experience

Contributor – NumPy

Oct 2025 | Remote

• Finalized linalg/fft deprecations (PR #29909) to enhance type safety for ML workflows
• Improved numerical stability for scientific computing and generative models (e.g., GANs) via clean SVD/FFT implementations

Machine Learning Trainee – ISTE HIT Student Chapter

Mar – Sep 2025 | Haldia

• Executed ML pipelines for student projects, boosting model accuracy and applying skills to real-world cases
• Mentored at BitsNBytes 2025 (ISTE), guiding participants through technical challenges and advanced DSA problem-solving

Contributor (Hacktoberfest) – LocalStack

Oct 2025 | Remote

• Normalized documentation structure for “Reproducible ML with Cloud Pods” tutorial by adding Introduction and Testing sections (PR #268)
• Enhanced developer onboarding by improving clarity and consistency in AWS emulation guides, reviewed and merged by core maintainers

Education

Haldia Institute of Technology
B.Tech in Computer Science and Engineering
2024 – Present
Expected Graduation: 2028 | CGPA: 8.59

Birbhum Zilla School
Higher Secondary (Class 12)
2020 – 2022
Grade: O (92.2%)

Projects

Fake Handwritten Digits Generation (GAN – MNIST)

GitHub

• Designed and implemented a Generative Adversarial Network (GAN) using TensorFlow/Keras, optimizing custom Generator & Discriminator models with

  tf.GradientTape

• Built a training visualization pipeline to generate GIFs for monitoring loss convergence and model performance
• Successfully generated realistic synthetic handwritten digits, demonstrating the potential of GANs in data augmentation
• Tools: TensorFlow, Keras, NumPy, Matplotlib, Python Notebook

AkBOT – AI Portfolio Chatbot

Live

• Developed a RAG-based chatbot using FastAPI, FAISS, LangChain, and Gemini API, enabling intelligent LLM-driven, context-aware portfolio Q&A
• Deployed a responsive web app on Render, improving recruiter engagement with dynamic project queries
• Tools: FastAPI, LangChain, ChromaDB, Gemini API, HTML, CSS, JavaScript, Render

Customer Sentiment Segmentation

Live

• Built an ensemble ML classifier with TF-IDF and SciPy sparse matrices, achieving 89.15% accuracy on large-scale reviews
• Segmented customer feedback into positive/negative/neutral classes, enabling data-driven business insights
• Tools: Python, scikit-learn, NumPy, SciPy

Video-RAG-Search – YouTube Video RAG Platform

GitHub

• Developed a Flask-based web application for YouTube audio transcription using Whisper, keyword extraction with Groq LLM, and semantic embeddings via SentenceTransformers stored in MariaDB
• Implemented semantic similarity search for transcript snippets with ranking, interactive UI, and caching via Flask-Caching
• Tools: Flask, MariaDB, Whisper, SentenceTransformers, yt-dlp, Groq API

Skills

Languages: Python, Java, C++, SQL, JavaScript

Libraries & Tools: NumPy, Pandas, Matplotlib, Seaborn, SciPy, scikit-learn, XGBoost, TensorFlow, Keras, PyTorch, OpenCV, Transformers, MLflow, Docker

AI/ML & Cloud Platforms: Hugging Face, LangChain, FastAPI, Streamlit, Gradio

Databases: MySQL, SQLite

Soft Skills: Teamwork, Leadership, Adaptability, Problem-Solving

Certifications

Certification	Provider	Date	Credential
Intro to Machine Learning	Kaggle	Jun 2025	Credential
Data Analysis	American Express (via Unstop)	Sep 2025	Credential

Skills from certifications: Random Forest, Model Validation, Machine Learning, Data Analysis, Business Analytics, Python