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Achievement: Pull Sharkx2Achievement: YOLO

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purvanshh/README.md

Purvansh Sahu

Backend · AI/ML · Machine Learning

About Me

  • 🎓 4th-year Undergraduate CS Student at Scaler School of Technology & BITS Pilani (2023 — 2027)
  • 🔬 Machine Learning Research Intern at IIT Madras (Jan 2026 — Present)
  • 🛠️ I build production-grade systems combining backend engineering with ML research, multi-agent orchestration, and observability
  • 📍 Based in Bangalore, India
  • 🌐 Portfolio: purvanshsahu.vercel.app

Experience

Machine Learning & Python Intern | Indian Institute of Technology Madras (Jan 2026 — Present, Remote)

  • Designed config-driven evaluation pipelines and conducted ablation studies on CNN-based aerial plant disease detection using drone imagery under distribution shift, collaborating with ICAR -- National Research Centre for Banana on field constraints.

Featured Projects

PRGuard AI

Webhook-driven multi-agent system orchestrating Style, Logic, and Security review agents in parallel to post confidence-scored inline feedback on GitHub PRs.

  • Tech: Python, FastAPI, Celery, Redis, ChromaDB, tree-sitter, Docker, GitHub Actions, Prometheus

SentinelOps AI

Incident reasoning system using LangGraph for multi-node state orchestration, Qdrant RAG dense retrieval, auto-degrading local model fallbacks, and token-gated tool execution safety.

  • Tech: Python, LangGraph, FastAPI, Qdrant, Celery, Redis, Ollama, PostgreSQL, Docker, Prometheus

Ollive

Full-stack AI gateway with cost-aware routing between local OSS (Qwen2.5) and frontier models, sub-300ms content safety via Llama Guard 3, PII redaction, and a GPT-4-as-judge evaluation harness.

  • Tech: Python, FastAPI, Next.js, Qwen2.5-0.5B, GPT-4, Llama Guard 3, Langfuse, Prometheus, Docker

AuditLend (ALICe)

Credit decision engine leveraging XGBoost with isotonic calibration (AUC-ROC 0.976) and SHAP explainability, featuring KS-based distribution shift detection and transactional audit trails.

  • Tech: Python, XGBoost, FastAPI, Celery, Redis, PostgreSQL, Docker, SHAP, Prometheus, GitHub Actions

DRISE

Layout-aware multimodal document intelligence system extracting structured JSON from unstructured PDFs using LayoutLMv3 and PaddleOCR, achieving 0.581 F1 score.

  • Tech: Python, PyTorch, LayoutLMv3, PaddleOCR, FastAPI, Docker, Pydantic, NetworkX

Technical Skills

Category Skills & Technologies
Languages Python, SQL, TypeScript, JavaScript, HTML5, CSS3, Bash
Frontend React, Next.js, Tailwind CSS, Shadcn/ui, Material UI, Redux Toolkit, TanStack Query (React Query), React Hook Form, Zod, Framer Motion, Axios
Backend & Infrastructure FastAPI, Celery, Redis, PostgreSQL, MongoDB, Docker, Kubernetes, GitHub Actions, CI/CD
ML & AI Systems LangGraph, RAG Pipelines, Multi-Agent Orchestration, PyTorch, Transformers, YOLOv8, XGBoost
Observability & Tools Langfuse, Prometheus, Grafana, MLflow, SHAP, tree-sitter, Ollama, Git

Connect With Me

Pinned Loading

  1. PRGuard-AI PRGuard-AI Public

    Multi-agent GitHub PR review system with parallel Celery workers (Style, Logic, Security agents), AST-based code parsing via tree-sitter, confidence arbitration with IoU-based span clustering, and …

    Python

  2. Github-Rag Github-Rag Public

    RAG pipeline over GitHub repositories, indexes codebases into a vector store and enables natural language querying over code, PRs, and documentation.

    Python

  3. AuditLend AuditLend Public

    Production-grade loan eligibility and credit decisioning backend featuring a configurable rule engine, borrower risk scoring, audit trails, and transactional outbox pattern for reliable event delivery

    Python

  4. Document-Intelligence-Engine Document-Intelligence-Engine Public

    Document understanding pipeline using LayoutLMv3 and PaddleOCR for structured data extraction from complex PDFs and scanned documents. FastAPI backend with Docker deployment.

    Python

  5. Transformer-from-scratch Transformer-from-scratch Public

    A from-scratch implementation of the Transformer architecture to understand attention mechanisms, embeddings, and training dynamics.

    Jupyter Notebook

  6. Portfolio-Machine-Learning Portfolio-Machine-Learning Public

    A curated collection of machine learning projects covering data preprocessing, model training, evaluation, and experimentation workflows.

    JavaScript