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Achievements

Achievement: StarstruckAchievement: Galaxy BrainAchievement: Pull Sharkx2Achievement: Pair ExtraordinaireAchievement: YOLOAchievement: Quickdraw

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ash-iiiiish/README.md

Ashish Kumar

AI/ML Engineer · Generative AI · Agentic Systems

Building AI systems that can reason, retrieve, use tools, and solve real-world problems.

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$ whoami

I'm a Computer Science undergraduate focused on Generative AI, agentic workflows, backend engineering, and applied machine learning..

I enjoy building beyond the chatbot layer: systems that retrieve knowledge, call tools, coordinate workflows, process visual information, and integrate AI into usable applications.

  • 🧠 Agentic AI: LangGraph workflows, state management, tool calling, and multi-step orchestration.
  • 🔎 Advanced RAG: Self-RAG/CRAG patterns, reranking, retrieval validation, and vector search.
  • 🔌 AI Tooling: Model Context Protocol (MCP), FastMCP, and tool-integrated applications.
  • ⚙️ Backend Engineering: Python, FastAPI, Django REST Framework, PostgreSQL, and asynchronous workflows.
  • 👁️ Applied ML: Computer vision, YOLOv8, OpenCV, and multimodal retrieval.

$ cat tech-stack.txt

Generative AI & Agentic Systems

LangChain LangGraph LangSmith LlamaIndex MCP Groq Ollama

Machine Learning & Computer Vision

PyTorch Scikit-learn XGBoost YOLOv8 OpenCV Hugging Face

Backend & Data Infrastructure

Python FastAPI Django PostgreSQL pgvector FAISS Redis

Engineering & Deployment

Docker Git Streamlit JavaScript C++ SQL

$ git status

  • 🔭 Currently working on: Agentic AI, enterprise knowledge retrieval, and AI-powered backend services.
  • 🌱 Currently learning: Better evaluation strategies, reliable agent orchestration, and production AI engineering.
  • 🤝 Open to: AI/ML engineering opportunities, GenAI internships, research collaborations, and meaningful open-source projects.
  • 💬 Let's discuss: RAG architecture, LangGraph, MCP, AI agents, computer vision, and Python backend engineering.

Have an interesting AI problem?

Let's build something useful.

Connect on LinkedIn Send an email



Designed around building, evaluating, and improving real-world AI systems.

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  1. Enterprise-Knowledge-Copilot Enterprise-Knowledge-Copilot Public

    LangGraph-powered enterprise assistant that combines RAG over your own documents, MCP tools (tickets, employee lookup, company policies, internal knowledge), Self-RAG / CRAG verification

    Python 12 3

  2. ShopLens.AI-Multimodal-RAG-E-commerce-Product-Assistant ShopLens.AI-Multimodal-RAG-E-commerce-Product-Assistant Public

    A modern, web-based AI assistant where users can : Upload a product image, Ask questions about its feature or alternatives, Gert answers with retrieved specs, links, and visual content. ChatGPT + G…

    Jupyter Notebook 16

  3. VIGIl.AI-Violence-WeaponDetectionTool VIGIl.AI-Violence-WeaponDetectionTool Public

    An AI-Powered System which can detect any type of violence occured in a video file that can be used in CCTV recordings to detect noise and provide necessary alerts.

    Python 8

  4. 30-basic-Machine-Learning-Projects 30-basic-Machine-Learning-Projects Public

    30 Ml projects : Covered Classification, Regression, Clustering, Ensemble Methods, NLP, Association Rule Mining, and Time Series Analysis.

    Jupyter Notebook 1

  5. CSE-Core-Subject-Chatbot CSE-Core-Subject-Chatbot Public

    A chatbot which can reply to all questions related to CSE core Subjects, This covers subjects such as Compiler Design, Cloud Computing, DSA, Computer Networks etcc.

    Jupyter Notebook 2

  6. master-repo-maths-cs-ai-compendium master-repo-maths-cs-ai-compendium Public

    Forked from HenryNdubuaku/maths-cs-ai-compendium

    Become a cracked AI/ML Research Engineer

    TypeScript 7