Sashit Vijay Open to roles

Useful systems with full-stack craft

I build full-stack products, machine learning systems, and computer vision tools with a focus on speed, clarity, and interfaces that feel calm enough to trust.

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type Focus = "full-stack" | "ml" | "cv";

interface Builder {
  name: "Sashit Vijay";
  base: "Arizona State University";
  style: "calm interfaces";
  ships: Focus[];
}

export const portfolio: Builder = {
  name: "Sashit Vijay",
  base: "Arizona State University",
  style: "calm interfaces",
  ships: ["full-stack", "ml", "cv"]
};

ready for the next build

CorpusForge v3 · 54M params

dataset: OpenWebText · 50M tokens layers: 8 · heads: 8 · d_model: 512 · ctx: 256 $ python train.py --model v3 --steps 5000 step 1000 · loss 5.82 step 3000 · loss 5.38 step 5000 · train_loss 5.18 val_loss 5.22 ✓ checkpoint saved · serving via FastAPI █

GPT decoder built from scratch

54M-parameter decoder-only transformer trained on 50M tokens of OpenWebText with no pretrained weights. 8 layers, 8 attention heads, 512-dimensional embeddings, Flash Attention, cosine LR with 200-step warmup, and AdamW with gradient clipping. Inference served via FastAPI.

PyTorchFastAPItiktokenFlash Attention

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Computer vision for match analysis

Processes Bundesliga footage through a YOLOv8 detection pipeline, KMeans shirt-colour team segmentation, optical-flow camera motion compensation, and perspective transform — producing real-world speed and distance per player from raw video frames.

PythonYOLOv8OpenCVKMeans

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Onsight Pipeline ep 382 – 777

NER SpaCy · entity extraction

Graph NetworkX · co-occurrence

Classify DeBERTa · ability types

Theme zero-shot · DeBERTa-large

Chat Llama 3.1-8B · LoRA

Five-module NLP pipeline

End-to-end LLM/NLP system analysing 395 episodes of One Piece dialogue. SpaCy NER for entity extraction, NetworkX character co-occurrence graph, fine-tuned DeBERTa ability classifier, zero-shot episode theme analysis with DeBERTa-large, and a LoRA-tuned Llama 3.1-8B chatbot — all unified in a single Gradio dashboard.

Llama 3.1DeBERTaSpaCyGradio

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SyncLink NFS · 3-tier distributed FS

clients: 50 concurrent · protocol: binary 9-byte header $ nm --storage SS_Root_0,SS_Root_1 network manager bound · routing ready $ client write /docs/report.pdf 2.1MB → SS_Root_1 · acked in 11ms $ client copy /docs/report.pdf /backup/ ✓ cross-server copy complete █

Distributed file system in C

Network File System written in C supporting 50 concurrent clients across a three-tier architecture — Clients, Network Manager, and Storage Servers. Binary protocol with a 9-byte header (1-byte type + 8-byte length). Full CRUD, cross-server copy, metadata retrieval, and backup management at the Network Manager layer.

CPOSIX SocketsBinary ProtocolNFS

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Built with clarity, taught with patience.

I am a computer science graduate from Arizona State University with a 4.0 GPA, Summa Cum Laude honors, and a mix of product, research, teaching, and systems experience.

My work tends to sit where useful engineering meets clear communication: backend systems that hold up, ML workflows that explain what they are doing, and frontends that do not make users fight the interface.

Send me a message

Tool garden

Python Java C++ C React Node.js Django Flask PyTorch OpenCV MongoDB MySQL NumPy Pandas scikit-learn MATLAB

Contact

Have a role, project, or idea that needs a builder?

sashitvijay@gmail.com Resume

Interactive · Three.js · 3D walkthrough

Enter Sakura House

A real-time 3D walkthrough of a lakeside Japanese home — explore every room at your own pace.