B.Tech (Hons.) CSE with Major in AI & ML @ MUJ
Ex-ML Research & Development @IIT-H (Vigil Labs) | Springer Nature Published Author | 3× Dean's List
-blue?style=flat-square){kind=icon}
Who I am:
- 🔬 Ex-ML Research & Development @ IIT-H (Vigil Labs) — Federated Learning for decentralized medical image classification
- Reduced communication overhead by 45%, used 55% fewer resources, surpassed baseline accuracy by 20% on complex non-IID real-world data
- 🎓 Pursuing B.Tech (Hons.) CSE with Major in AI & ML @ Manipal University Jaipur
- 🏆 3× Dean's List — Excellence in Academics & Off-campus Achievements
- 📄 First Author — Published by Springer Nature on Barbell Exercise Recognition (Computer Vision + HAR)
What I build:
- 🧠 Building at the intersection of ML, NLP, Computer Vision, and Generative AI
- 🔍 Specializing in RAG pipelines, Federated Learning, LLM fine-tuning, and AI in healthcare
- 📈 Exploring SLMs, VLMs, model optimization, and LLM deployment at scale
Let's connect:
- 💬 Ask me about LLMs, LangChain, LangGraph, Federated Learning, or MLOps
- 🤝 Open to collaborations on AI research, impactful projects, and roles in ML/AI
|
High-performance FastAPI microservice achieving 90% reduction in prediction latency (50ms → 5.4ms). Improved False Positive precision from 6% to 78% using class-weighted XGBoost evaluated via MLflow. Full production observability via Prometheus + Grafana, Dockerized for AWS deployment. Stack: Python, FastAPI, XGBoost, MLflow, Docker, AWS, Prometheus, Grafana |
RAG pipeline with sub-second query latency and 95% relevance accuracy across 12,000+ anime entries using LangChain, Groq LLM, and ChromaDB. Automated ETL with HuggingFace Sentence Transformers. Deployed on GCP with Kubernetes, maintaining 99.5% uptime with Grafana monitoring. Stack: Python, LangChain, Groq LLM, ChromaDB, Streamlit, Docker, Kubernetes, GCP |
|
First-authored research published at ICDEC 2024 (Springer Nature). Engineered a robust ML system for barbell exercise classification and repetition counting using MetaMotion sensor data, achieving over 90% accuracy through comprehensive feature engineering and outlier detection pipelines for precise human activity recognition. Keywords: Computer Vision, Human Activity Recognition, Sensor Data, Feature Engineering, Classification |
- 🧠 Exploring Generative AI, LLMs, and advanced LLMOps deployment strategies
- 🔬 Researching Federated Learning applications in healthcare and privacy-preserving AI
- 🤏 Researching SLMs (Small Language Models) and VLMs (Vision-Language Models) and their real-world applications
- 🛠️ Building and deploying end-to-end MLOps and LLMOps pipelines
- 💡 Applying NLP & CV to solve real-world problems in healthcare and beyond
🔬 Research Experience
🏛️ ML Research & Development Intern — VIGIL Labs, IIT Hyderabad (Apr 2025 – Jul 2025)
- Engineered a decentralized Federated Learning model for medical image classification & segmentation
- Surpassed baseline test accuracy by 20% on complex non-IID real-world medical data
- Reduced global communication round time by 45% and utilized 55% fewer resources than baselines
- Orchestrated secure ML workflows to address data heterogeneity and strict distributed data privacy requirements
📄 First Author — Springer Nature Publication (ICDEC 2024)
- Published research on barbell exercise classification & repetition counting using MetaMotion sensor data
- Achieved >90% accuracy through comprehensive feature engineering, outlier detection, and Human Activity Recognition pipelines
- View Paper →
🏆 Honors & Awards
- 🎓 Dean's List for Excellence in Academics (Highest GPA) — Manipal University Jaipur
- 🏅 2× Dean's List for Excellence in Off-campus Achievements — Manipal University Jaipur
- 🤝 Strong academic foundation in CS & AI combined with hands-on industry and research impact
☁️ MLOps & Deployment Expertise
End-to-end experience taking AI projects from development to large-scale production:
- MLOps: MLflow, DVC, DAGsHub, Airflow, Astro Airflow, TaskFlow, BentoML, Grafana
- LLMOps: LangSmith, LangServe, LangGraph, LangChain
- Cloud: AWS (S3, EC2, IAM, RDS), GCP, Azure
- Containers: Docker, Kubernetes
| Category | Tools / Frameworks |
|---|---|
| AI & LLMs | GPT, BERT, Titan, Hugging Face Transformers, LLM Fine-tuning, RAG Pipelines |
| Deployment | Flask, Streamlit, FastAPI, BentoML |
| Visualization | Tableau, PowerBI, Plotly, Seaborn, Grafana |
| Dev Tools | Git, GitHub, Docker, Postman, MLflow, DVC, DAGsHub, Selenium |
| Collaboration | Jira, SCRUM, Agile |
| Domains | ML, DL, NLP, CV, Generative AI, LLMs, Federated Learning, MLOps, LLMOps, Healthcare AI |
If you find my projects helpful or interesting:
- ⭐ Star my repositories
- 🔔 Follow me on GitHub for updates
- 🤝 Collaborate on AI/ML research and projects
- 💬 Share with others who might benefit
💭 "Render thy labor as sacred offering unto the Most High; and lo, thy toil shall cease to be burden, becoming instead eternal joy"
⭐ If you find my work interesting, consider starring my repositories!
🤝 Open to collaborations, research opportunities, and roles in ML/AI/Healthcare
Made with ❤️ by Divyansh Pandey
💼 Seeking opportunities in world of applied ML/NLP/CV research | 🌐 Let's build something amazing together!