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© 2026 Jeet Majumder

Jeet

Hi, I'm

Jeet Majumder

Machine Learning & AI Practitioner. Focused on creating scalable, innovative solutions with elegance and efficiency.

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Professional portrait of Jeet Majumder

My Journey

About Me

I am a Data Scientist and AI Practitioner, passionate about building intelligent, data-driven systems that turn complex ideas into practical, real-world solutions. My experience spans machine learning–powered applications, from experimentation and system design to integration and deployment, with a strong focus on reliability and scalability.

Enjoys solving challenging problems at the intersection of data, algorithms, and software, with a strong focus on continuous learning, clean system design, and applying emerging technologies to build effective, production-ready solutions.

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Education & Milestones

B.Tech Computer Science & Engineering

Brainware University

2021 - 2025

Specialization in Data Science

Higher Secondary

Ganrapota High School (H.S.)

2019 - 2021

Pure Science stream

Technical Skills

90%
Machine Learning (ML)
85%
Deep Learning (DL)
75%
Natural Language Processing (NLP)
70%
Computer Vision (CV)
70%
Large Language Models (LLMs)
70%
Retrieval Augmented Generation (RAG)
70%
Agentic AI Systems
65%
Docker & Kubernetes
70%
Cloud Deploy (AWS / Render)
70%
CI/CD (GitHub Actions)
60%
DVC & MLflow
65%
Model Registry & Versioning
80%
Python
75%
SQL & Databases
75%
FastAPI / Flask
80%
Feature Engineering
70%
Data Pipeline Design

Impact & Telemetry

0+

Global Reach

Website Visits

0

Portfolio

Total Projects

0+

Production

Deployments

0+

Intelligence

Models Trained

My Engineering Workflow

From messy unstructured data to lightning-fast deployments.

1

Data Ingestion

Collect, validate & version raw datasets from multiple sources.

2

Feature Engineering

Transform, normalize & select features for optimal model input.

3

Model Training

Train & tune models with experiment tracking via MLflow / DVC.

4

Evaluation

Rigorous validation, A/B testing & performance benchmarking.

5

CI/CD & Registry

Automated pipelines, version-controlled model registry on S3.

6

Cloud Deploy

Containerized deployment on AWS EC2 / Render with monitoring.

1

Document Ingestion

Parse PDFs, DOCX, URLs & clean content into structured chunks.

2

Chunking & Embedding

Semantic splitting & vector embedding via sentence transformers.

3

Vector Store

Store & index embeddings in FAISS / ChromaDB for fast ANN search.

4

Retrieval & Re-ranking

Cosine similarity retrieval + cross-encoder re-ranking for precision.

5

LLM Generation

Grounded response synthesis with chain-of-thought prompting.

6

API Serving

Low-latency FastAPI endpoint with streaming & rate limiting.