|| Software Engineer || Data Scientist || AI/ML/DL || CS Practitioner ||
Bachelor of Engineering in Information Technology (BE-IT)
Master of Business Administration in Business Analytics (MBA-BA) — Pursuing
Open to collaboration on AI / ML / Deep Learning projects
Terminal-based portfolio
Install and explore directly from your terminal:
pip install MohitJan
Run: mohitjan
OR
Run: python -m MohitJan.main
| Featured AI & GenAI Projects |
|---|
| Project Name | Description | Technologies Used | Key Concepts | Status | Demo |
|---|---|---|---|---|---|
| Multi-Agent Productivity Assistant | Developed a cloud-native multi-agent AI assistant capable of managing tasks, notes, and scheduling through coordinated AI agents. Built scalable REST APIs and deployed the application using Google Cloud Run. |      |
Multi-Agent AI, Agentic AI, Large Language Models (LLMs), FastAPI, Cloud Run, REST APIs, Cloud Deployment | 🟢 Production Ready | Live API |
| ResAlloc – Smart Resource Allocation & Workforce Intelligence | Built an AI-powered cloud-native platform that converts unstructured reports into structured tasks, predicts urgency using Machine Learning, and intelligently allocates resources in real-time for NGOs and enterprise workforce management. |  |
Large Language Models (LLMs), Classification, Risk Prediction, Resource Allocation, Prompt Engineering, Cloud Deployment | 🟡 Prototype | Live Demo |
| Prompt-to-Production (NASSCOM Agentic AI Hackathon) | Contributed production-grade AI reliability workflows by implementing complaint classification, policy summarization, budget analysis, and document QA systems with explainability, validation-first pipelines, and hallucination mitigation. |  |
Prompt Engineering, Agentic AI, LLM Applications, Explainable AI (XAI), Validation-first AI, Hallucination Mitigation | 🟢 Completed | — |
| Machine Learning & Data Science Projects |
|---|
| Project Name | Description | Technologies Used | Algorithms Used |
|---|---|---|---|
| Employee Performance Analysis (IABAC) | Analyzed employee performance data to identify key productivity drivers, trends and patterns and support data-driven decision making. |     |
Principal Component Analysis (PCA), Decision Trees, Random Forest, Support Vector Machine (SVM), Gradient Boosting, XG-Boost, K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) |
| RiceLeafDiseaseDetection | Built a computer vision model to classify rice leaf diseases, supporting early detection based on images. |   |
Neural Networks, Convolutional Neural Network (CNN) |
| Portugese Bank Marketing | Developed predictive models to identify customers likely to subscribe to term deposits, improving campaign targeting efficiency. | |
Logistic Regression, Gradient Boosting, XG-Boost, Decision Trees, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) |
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| Topic | Notes |
|---|---|
| Pandas | - ************************************************************************************************************** |
| Numpy | - **** |
| Model Training | - *********** |
| Evaluation Metrics | - ****** |
| Deployment Strategies | - ********** |
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| Project DS/AI/ML |
|---|
| Project Name | Description | Technologies | PyPI (Package Name) |
|---|---|---|---|
| ChatBot | Creating a simple rule-based chatbot using Python. This chatbot can understand user input and provide predefined responses based on the detected intent of the input. |     |
Uploading Package soon |
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