Agentic AI
Engineering autonomous systems using LangGraph and Multi-Agent frameworks for complex decision-making.
Engineering autonomous systems using LangGraph and Multi-Agent frameworks for complex decision-making.
Designing conversational interfaces with sophisticated Memory Management and privacy controls.
Production-grade Voice AI Agents developed for real-time telephony and complex interactions.
Implementing Convolutional Neural Networks (CNN) and advanced architectures for image recognition and classification.
Extracting actionable insights from raw data using statistical modeling and interactive dashboards.
Building advanced Neural Architectures for Time-Series Forecasting (LSTM) and complex classification tasks.
Specializing Large Language Models (LLMs) using Low-Rank Adaptation (LoRA) and PEFT techniques.
Solving real-world business problems with Predictive Modeling and classical ML algorithms.
Implementation of the standardized protocol for connecting AI models to external data systems.
Rigorous benchmarking of LLMs using metrics like BLEU, ROUGE, and Perplexity.
End-to-end Text Processing pipelines, Semantic Analysis, and Transformer implementations (BERT/GPT).
Architecting systems that ground LLMs in private data using Vector Databases (Pinecone, FAISS).
Deep dives into model internals: Building Multi-Head Attention mechanisms from the ground up.
Developing interactive Frontends for AI applications using Streamlit and Gradio.
Deploying and hosting open-source AI applications on private servers. Examples of self-hosted platforms include Ollama, Open WebUI, Flowise, n8n, Bolt, Browser Use, Cal.com, Excalidraw, and Supabase.