archit@portfolio: ~

archit@portfolio:~$ whoami

Archit Konde

Machine Learning Engineer

█

open to opportunities

archit@portfolio:~$ █

↓  scroll to explore
$ cat about.txt

I'm a Machine Learning Engineer with a background in Electrical & Computer Engineering. My focus is on one of the most consequential problems of our time — the intelligence problem. I want to build AI systems that help people tackle complex, high-impact challenges.

My work sits at the intersection of research and engineering — from understanding the mathematical foundations of neural networks to building systems that push the boundaries of what AI can do. Long-term, I'm working toward meaningful contributions to AGI/ASI research and development.

Currently based in Waterloo, ON — the heart of Canada's AI and tech ecosystem.

$ cat experience.log
publication

Fundamentals of Neural Networks

International Journal for Research in Applied Science & Engineering Technology (IJRASET)

  • Authored a research paper examining the architectural and mathematical foundations of neural networks
  • Covered activation functions, backpropagation, gradient descent, and network topologies
Google Scholar ↗
volunteer

Data Analyst

City of Windsor — Affordable Transit Program

  • Cleaned and preprocessed transit ridership datasets to ensure data quality and consistency
  • Built dashboards to visualize usage patterns and surface actionable insights for program planning
$ cat education.txt
MEng

Electrical & Computer Engineering

University of Windsor — Canada

B.E

Computer Engineering

University of Mumbai — India

$ cat skills.json
"languages": [
Python C C++ Java MATLAB
],
"ai_ml": [
PyTorch TensorFlow scikit-learn NumPy Pandas
],
"tools_and_cloud": [
AWS Git GitHub Jupyter
],
"currently_learning": [
Transformers LLM internals microGPT → nanoGPT
]
$ ls ./projects/
01 // live

supportops_ai_monitor.py

Simulates enterprise AI support workflows — ticket triage via GPT-4o-mini, API health logging, and an operational observability dashboard. Runs in simulation mode with no API key.

python streamlit openai sqlite plotly
02 // live

rag_from_scratch.py

Full RAG pipeline built from scratch — recursive chunking, BM25 + dense hybrid retrieval with RRF, cross-encoder reranking, and grounded generation. No LangChain, no LlamaIndex.

python transformers numpy streamlit bm25
03 // initializing

project_name.py

Description loading…

python scikit-learn

→ projects are actively being built. follow along on GitHub ↗

$ ls ./blog/
MAR 2026 // learning writeup

Building SupportOps AI Monitor — What I Learned

Architecture decisions, bugs found, deployment challenges, and honest answers to questions I couldn’t answer at first. Covers applymap() removal in pandas 2.2, SQLite ephemeral containers, Streamlit’s PWA limitations, and more.

python streamlit openai sqlite
MAR 2026 // learning writeup

Building RAG From Scratch — A Complete Technical Deep-Dive

Every algorithm in a RAG pipeline derived from first principles. BM25 with Robertson-Walker IDF, mean pooling math, cosine similarity as dot product, Reciprocal Rank Fusion, cross-encoder reranking, and evaluation metrics. No LangChain, no LlamaIndex.

python transformers numpy information retrieval
$ cat contact.txt

I'm actively looking for opportunities in AI/ML engineering and research. If you're working on something interesting — let's talk.

email → architkonde19@gmail.com github → Archit-Konde ↗ linkedin → architkonde ↗ twitter / x → @architkonde ↗ google scholar → Archit Konde — IJRASET ↗