Anik Chand
Kolkata, India |
anikchand461@gmail.com
| +91 9153772355 |
anikchand.vercel.app
|
linkedin.com/in/anikchand461
|
github.com/anikchand461
|
kaggle.com/anikchand
Summary
Machine Learning Engineer specializing in NLP, LLM applications and model deployment., Experienced in building pro-duction ML pipelines, RAG systems and hyperparameter optimization libraries using python, fastAPI and TensorFlow.
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
2024-Present
B.Tech in Computer Science and Engineering, Expected Graduation: 2028 , CGPA : 8.59
Birbhum Zilla School
2020-2022
Higher Secondary (Class 12), Grade: O (92.2%)
Projects
lazytune – Fast Hyperparameter Optimization for scikit-learn
GitHub · Live Demo
• Developed LazyTune, a smart hyperparameter optimization library (available on PyPI) that achieves speedup over GridSearchCV with near-identical final performance.
• Implemented a multi-stage pipeline: quick screening with cross-validation, performance-based ranking, early pruning of poor configurations (prune_ratio), and full training on top candidates.
• Tools: Python, scikit-learn, NumPy, pandas, FastAPI (backend), HTML/CSS/JavaScript (frontend), Vercel
AkBOT – AI Portfolio Chatbot
GitHub · Live Demo
• Developed a RAG-based chatbot using FastAPI, FAISS, LangChain, and Gemini API, enabling intelligent LLM-driven, context-aware portfolio QA.
• 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
GitHub · Live Demo
• 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
Certifications
Intro to Machine Learning
Kaggle | Jun 2025 |
Credential
Skills: Random Forest, Model Validation, Machine Learning
Introduction to Model Context Protocol
Anthropic | Mar 2026 |
Credential
