I build end-to-end AI systems from data & models to scalable, production-ready applications. My work sits at the intersection of Machine Learning, Deep Learning, LLMs, and Full-Stack Engineering.
Actively building and maintaining multiple AI-driven projects, focused on real-world deployment, explainability, and scalable system design:
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OmicsAI – Multi-Omics AI SaaS Platform
👉 https://github.com/rishabh-108272/OmicsAI-main- Multi-omics data integration (RNA-Seq, clinical, molecular data)
- Explainable AI using SHAP & LIME
- AI-assisted research and healthcare workflows
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CraftIQ – AI Tools SaaS Platform
👉 https://github.com/rishabh-108272- Full-stack AI SaaS platform with multiple AI-powered utilities
- Authentication, role-based access, CI/CD, cloud deployment
- Focus on production-ready AI features
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LLM & Agentic AI Experiments
👉 https://github.com/rishabh-108272- Retrieval-Augmented Generation (RAG) pipelines
- Agent-based workflows using LangChain & LangGraph
- Research-focused experimentation with real application goals
- Full-Stack Engineering (Production-first mindset)
- Scalable backend systems (Django, FastAPI)
- Modern frontend workflows (React)
- Large Language Models (LLMs)
- Understanding the mathematical & architectural foundations
- Agentic workflows using LangChain & LangGraph
- MLOps
- Model lifecycle management
- Deployment, monitoring & CI/CD for ML systems
- ✔️ End-to-end AI / ML solutions
- ✔️ Custom Deep Learning models (CV, NLP, Healthcare AI)
- ✔️ LLM-powered applications (RAG, agents, assistants)
- ✔️ Explainable AI for regulated domains
- ✔️ Backend APIs for ML systems (Django / FastAPI)
- ✔️ AI-powered SaaS & research platforms
- ✔️ Taking ideas from prototype → production
Artificial Intelligence, Machine Learning, Deep Learning, NLP, Computer Vision, LLMs, Full-Stack AI Systems, and Production ML
🔍 I care about clean architecture, explainability, and building AI systems that actually work in the real world.