I'm an AI/ML engineer who builds LLM applications end to end: RAG pipelines, fine-tuned models and multi-step agents, shipped with Docker and AWS. I also teach the Full Stack AI Engineer course at DrippinAI Labs.
const punit = {
role: "AI/ML Engineer",
languages: ["Python", "JavaScript", "SQL"],
focus: ["Agentic AI", "RAG", "LLM Fine-tuning", "Generative AI"],
agents: ["LangGraph", "LangChain", "CrewAI", "n8n"],
retrieval: ["FAISS", "Pinecone"],
models: ["LLaMA", "Hugging Face Transformers", "Groq"],
classicalML: ["scikit-learn", "TensorFlow", "Keras", "PyTorch"],
deploy: ["Docker", "AWS", "Streamlit"],
currentFocus: "Production-grade agentic AI systems",
funFact: "There are two ways to write error-free programs; only the third one works"
};- 🔭 Ex-AI Intern at Industrial Light & Magic (ILM), Production Technology, Mumbai
- 🎓 AI Lead Instructor at DrippinAI Labs, Full Stack AI Engineer course
- 🌱 Going deeper on agent orchestration, evaluation and LLM fine-tuning
- 💬 Ask me about RAG, agentic AI, LLMs, Python and ML
- 📫 Contact: punitgavali29@gmail.com
I design agents as explicit state machines (LangGraph), not one giant prompt. Example flow from my recruitment pipeline:
flowchart LR
A[Resume + Job Description] --> B[Parser Agent]
B --> C[Matching & Scoring Agent]
C --> D{Score above threshold?}
D -- Yes --> E[Shortlist + Summary]
D -- No --> F[Polite Rejection Draft]
E --> G[n8n: Notify Recruiter]
F --> G
| Project | What it does | Stack |
|---|---|---|
| 🧑💼 Agentic Recruitment Pipeline | Multi-step agent that parses, scores and routes candidates | LangGraph, n8n, Groq |
| 🏢 Enterprise RAG Chatbot | Support assistant that cut support workload by about 42% | RAG, vector DB, LLMs |
| 🏥 MedQueryDoc AI | Medical document Q&A | LLaMA-2, Pinecone |
| 🧾 Receiptorium Chatbot | Fine-tuned LLaMA 3.2 assistant for a work-tracking platform | LLaMA 3.2, fine-tuning |
| 💳 Transaction Anomaly Detection | Flags suspicious financial transactions on a live dashboard | Isolation Forest, Streamlit |
| 🛡️ PhisProbe | Phishing link detection, published in IJNTI, IIT Roorkee Productathon finalist | Streamlit, GenAI |
- 🎯 Finalist at Productathon AI Hackathon, IIT Roorkee
- 📝 Published research paper in IJNTI Journal: "PHISPROBE: Phishing Link Detection Using Streamlit and Generative AI Approach"
- 🏅 IBM AI Engineering Professional Certificate (2024)
- 🎓 B.E. in Artificial Intelligence & Data Science, FR CRCE Mumbai (8.82 CGPA)
