Machine Learning Engineer | NLP & GenAI Systems Builder | MLOps Explorer

🎓 3rd-Year Data Science Student
📍 Pune, India

I design and build end-to-end Machine Learning & NLP systems — from raw data to deployable applications.

I focus on:

• Production-oriented ML pipelines
• NLP & Retrieval-Augmented Generation (RAG) systems
• Agentic AI workflows
• Applying ML in real-world industrial settings

I care about system design, reproducibility, and practical impact — not just notebooks.

• Python
• C++
• SQL

• NumPy, Pandas
• Matplotlib, Seaborn
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Data Preprocessing
• Power BI

• Scikit-learn (Regression, Classification, Pipelines)
• Cross-Validation & Model Evaluation
• Scaling & Transformations
• End-to-End ML Workflows
• Deep Learning Foundations

• LangChain
• LangGraph
• LangSmith
• Prompt Engineering
• Structured Output Parsing
• Agentic Workflows
• Retrieval-Augmented Generation (RAG)

• Git & GitHub
• Streamlit
• Docker (in progress)
• FastAPI (in progress)
• Jupyter Notebook

Digital Image Forensics | Signal Processing

Wavelet-domain PRNU-based system to verify if an image originates from a registered camera sensor.

✔ Extracted sensor-level PRNU fingerprints
✔ Applied wavelet-domain noise modeling
✔ Designed verification & matching pipeline
✔ Focused on signal-level feature extraction

Demonstrates: Feature engineering, signal processing, system validation logic.

Industrial AI | RAG | LLMOps

LLM-powered maintenance assistant for flour manufacturing plants.

✔ Custom document ingestion pipeline
✔ Vector database integration
✔ Retrieval-Augmented Generation (RAG)
✔ Context-aware Q&A over technical manuals
✔ Designed for real-world industrial troubleshooting

Demonstrates: System architecture, RAG pipelines, applied GenAI.

NLP | End-to-End MLOps | Scalable ML System

Currently building a production-oriented NLP pipeline for large-scale customer complaint analysis.

✔ Text preprocessing & cleaning pipeline
✔ Feature extraction & vectorization
✔ Sentiment analysis
✔ Topic intelligence & issue clustering
✔ Simulated streaming workflow
✔ Designed with MLOps principles

Goal: Build a deployable, scalable NLP intelligence platform.

• Dockerized ML systems
• FastAPI-based model deployment
• Advanced NLP pipelines
• Scalable RAG architectures
• End-to-End ML deployment workflows

✔ I build systems, not just models
✔ I focus on production readiness
✔ I enjoy debugging complex ML environments
✔ I apply ML to real industrial use-cases
✔ I continuously evolve projects beyond academic scope

💼 Open to Machine Learning / NLP / GenAI Internships