Machine Learning & NLP Engineer in training
I build and deploy ML systems — from raw data to working apps.
End-to-end ML systems — not just notebooks. Every project is deployed and results-focused.
Built a full NLP pipeline — tokenisation, stopword removal, stemming, TF-IDF — feeding a Voting Classifier of SVM, Decision Tree, and Extra Trees. Deployed as a live Streamlit app.
Combined TF-IDF features with behavioural metadata to classify 6 emotional states and predict intensity, with uncertainty modelling and a 3-way ablation study. Flask app recommends real actions.
Two-engine recommender — popularity-based + collaborative filtering via cosine similarity — trained on 1.1M+ ratings across 271,360 books. Returns top-5 personalised picks. Deployed with Flask.
Built with Rasa using intent classification, entity extraction, and dialogue management to hold natural multi-turn conversations. Handles context across several conversational turns.
An LLM-powered tool that reviews resumes against job descriptions and gives actionable improvement suggestions. Built with LangChain and RAG — bringing LLM reasoning to a practical hiring-pipeline problem.
Final-year B.Sc. CS student (CGPA 7.75) with a 9-month AI Minor from IIT Ropar, specialising in end-to-end ML systems — from feature engineering and model selection to deployment.
I enjoy taking models past the notebook stage into something a real person can use. Not just accuracy metrics — but working apps, real decisions, measurable outcomes.
Currently deepening my skills in LangChain and RAG pipelines, building towards production-ready LLM-powered systems.
Core CS fundamentals plus a focused AI specialisation — the two tracks that shape how I build.
9-month specialisation covering Machine Learning, Deep Learning, NLP, Computer Vision, statistical modelling, end-to-end AI system design, hyperparameter tuning, and deployment pipelines.
CGPA 7.75 · Coursework in Data Structures, Algorithms, DBMS, and Operating Systems. Final-year, graduating 2027.
Primarily looking for an ML/AI internship — and open to freelance or collaborative projects in:
Classification, prediction, and recommendation systems built for your specific data and use case — not generic templates.
Turn unstructured text into structured insights — spam detection, sentiment analysis, intent classification, and entity extraction.
Clean, explore, and visualise messy datasets into clear, actionable findings your team can actually act on.
Take a trained model from a notebook to a working web app — Flask or Streamlit, ready for real users.
The full toolkit behind every project — from feature engineering through deployment.
Looking for an ML internship or a freelance collaborator for your next data/AI project? I'm available and happy to talk.
ML internship, part-time role, or a well-scoped freelance project — if it involves building something with data, let's talk.