Mixtral 8x7B — A Deep Dive
A detailed comparison of Mixtral 8x7B with LLaMA 2, and an implementation of an optimized Mixture of Experts (MoE) layer in PyTorch.
Read on SubstackMachine Learning Engineer
Specializing in AI Agents and RAG systems. Building production agentic platforms with Claude Agent SDK, LangGraph, and MCP, and sharing deep technical implementations from first principles.
I'm a Machine Learning Engineer specializing in AI Agents and Retrieval-Augmented Generation (RAG). Currently at Verloop.io, I'm shaping the team's agent-platform strategy and building production RAG systems that power autonomous customer support for enterprise clients across e-commerce, banking, and healthcare.
I work hands-on with modern agentic frameworks — Claude Agent SDK, LangGraph, and Deep Agents — alongside Model Context Protocol (MCP), function calling, structured outputs, sub-agent orchestration, human-in-the-loop approval flows, and agent observability. On the retrieval side, I design evaluation frameworks for embedding models, cross-encoder rerankers, and late-interaction models, with production pipelines for complex document parsing, Weaviate vector search, and context engineering.
I also believe in understanding AI systems at a fundamental level. Through my technical blog, I break down topics like attention mechanisms (MHA, GQA, MLA), KV-cache, the LLaMA-2 architecture, and Mixtral 8×7B MoE, providing complete PyTorch implementations from scratch. I focus on building with clarity, sharing knowledge openly, and making advanced ML techniques accessible to practitioners.
Technologies and areas I work with
My professional journey and key achievements
Verloop.io
Building agentic AI platforms and production RAG systems for autonomous customer support at enterprise scale.
Monsoon CreditTech
Developed ML models for credit risk assessment in fintech.
BML Munjal University
Major in Computer Science and Engineering. Published research in Nature Scientific Reports.
Open-source projects spanning LLMs, ML, and data engineering
Autonomous research agent that decomposes queries, executes multi-turn tool calling with web search and arXiv, and validates completeness through self-reflection. Features real-time Streamlit UI.
Natural language interface for Weaviate vector databases through Claude using Model Context Protocol. Enables intuitive database exploration via conversation with 9 inspection tools.
Deep learning OCR system with ResNet encoder and Transformer decoder (14M parameters). Achieves 70% error reduction via augmentation. Deployed as FastAPI microservice on GCP with monitoring.
Latest technical articles combining theory and practice
A detailed comparison of Mixtral 8x7B with LLaMA 2, and an implementation of an optimized Mixture of Experts (MoE) layer in PyTorch.
Read on SubstackStep-by-step guide to building the LLaMA model from scratch in PyTorch, with in-depth explanations of each essential component.
Read on SubstackUnderstanding the evolution from Multi-Head Attention to modern inference optimizations
Read on SubstackI'm always interested in discussing AI agents, RAG systems, LLMs, ML engineering, or potential collaborations. Feel free to reach out!
Or send me an email directly at ashishgy77@gmail.com