I’m an early-career AI Engineer focused on building practical AI applications using Python, Generative AI, RAG, and Agentic AI.
I build end-to-end systems spanning LLM applications, retrieval pipelines, AI agents, machine learning, and deep learning - from data processing and model development to deployment and user-facing applications.
I’m currently focused on building production-oriented AI systems and contributing to open-source projects.
I particularly enjoy solving concrete problems with neural networks and building intelligent systems that create measurable impact.
- Python
- SQL
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- AI Agents
- Multi-Agent Systems
- Prompt Engineering
- Embeddings
- Vector Databases
- Local LLMs
- Classification
- Regression
- Clustering
- Feature Engineering
- Model Evaluation
- Hyperparameter Optimization
- TensorFlow
- Keras
- CNNs
- Transfer Learning
- NLP
- Computer Vision
- LangChain
- FAISS
- Ollama
- Groq API
- Streamlit
- Git & GitHub
- Jupyter Notebook
- VS Code
- MySQL
Built a domain-specific conversational assistant using Retrieval-Augmented Generation. Implemented FAISS vector search, local embeddings with Ollama, and Groq-hosted LLMs to provide accurate, context-aware responses through a Streamlit application.
Technologies: Python, LangChain, FAISS, Ollama, Groq, Streamlit
Developed a collaborative AI agent framework consisting of specialized web and finance agents capable of tool usage, information retrieval, and financial analysis using real-time market data.
Technologies: Phi Framework, Groq, DuckDuckGo, Yahoo Finance, Agent Orchestration
Designed an NLP pipeline to discover and group related news events using sentence embeddings and unsupervised learning techniques. Generated event timelines and automated labeling for improved information discovery.
Technologies: Python, NLP, Sentence Embeddings, Scikit-learn, Clustering
Built a deep learning system for retinal image classification using EfficientNetB0 and TensorFlow.Applied transfer learning and class weighting to improve minority-class detection and deployed the model through a Streamlit application.
Technologies: Python, TensorFlow, EfficientNetB0, Streamlit
Contributed to the Optiland open-source project by identifying and correcting an incorrect derivative formula in the Custom Surface Types tutorial.
- Submitted GitHub PR #752
- Numerically verified the corrected derivatives
- Passed automated repository checks
- Generative AI & LLM Applications
- Agentic AI & Multi-Agent Systems
- Retrieval-Augmented Generation
- AI Application Development
- Machine Learning & Deep Learning
- NLP & Computer Vision
- AI Deployment & MLOps
Oct 2025 – Present
Working on practical machine learning, deep learning, Generative AI, RAG, and AI agent applications using Python and modern AI frameworks.
LinkedIn: https://www.linkedin.com/in/praband-kumar-t-40405a3b0
Email: praband10@gmail.com