AI/ML Engineer by passion 🤖 and an iOS Developer by experience 📱

After spending more than six years building iOS applications with Swift, I shifted my focus to Artificial Intelligence and Machine Learning. Today, I'm interested in designing AI applications that combine machine learning research with practical software engineering.

My current areas of interest include Large Language Models, Information Retrieval, Multi-Agent Systems, Natural Language Processing and Deep Learning.

• Large Language Models (LLMs)
• Multi-Agent AI Systems
• Retrieval-Augmented Generation (RAG)
• Dense Retrieval
• Information Retrieval
• Natural Language Processing
• Deep Learning
• MLOps

An AI-powered recruitment application that extracts structured resume information using parallel AI agents and performs semantic Resume ↔ Job Description matching.

Built with

Python · LangGraph · FastAPI · Transformers · Docker

Fine-tuned a BERT-based Dense Retriever on the MS MARCO dataset and evaluated its retrieval performance across multiple BEIR benchmark datasets.

Highlights

• Fine-tuned on MS MARCO
• Evaluated on BEIR benchmarks
• Dense Retrieval
• Semantic Search

A multi-agent AI application that combines player statistics, match information and football news to generate comprehensive match analysis.

Built with

LangGraph · FastAPI · Groq · NVIDIA API · Docker

A Retrieval-Augmented Generation (RAG) application using semantic chunking, FAISS vector search, reranking and LLM-powered question answering.

• Python
• PyTorch
• Transformers
• LangGraph
• LangChain
• Sentence Transformers
• Hugging Face
• FAISS
• Scikit-learn
• Pandas
• NumPy

• FastAPI
• REST APIs
• Docker
• Git
• GitHub

• Swift
• UIKit
• AVFoundation
• Core Data
• Firebase
• MVVM

• LinkedIn — https://www.linkedin.com/in/imihir/
• Hugging Face — https://huggingface.co/Innovatewithapple
• Email — [email protected]