Anupam
M Hegde
I build end-to-end AI systems — from data pipelines and machine learning models to autonomous agents and deployable applications focused on correctness and real-world use.
Core Capabilities
What I can build and deliver for your organization
Machine Learning Systems
Design and implement end-to-end ML systems from data preprocessing to model deployment with focus on reliability and performance.
Data Pipelines & Preprocessing
Build robust data processing workflows that handle real-world messy data and scale efficiently across different environments.
Intelligent Agent Systems
Develop autonomous agents that can reason, plan, and execute complex tasks with minimal human intervention.
Computer Vision
Create vision systems that process and understand visual data for classification, detection, and analysis applications.
AI Applications & Deployment
Transform research prototypes into production-ready applications with proper testing, monitoring, and scalability.
Featured Projects
End-to-end systems that solve real problems through engineering excellence
Quant Solver – Autonomous Aptitude Generator
TL;DR: AI system that automatically generates error-free math questions using multiple validation agents
3x faster question validation, eliminated human error in test creation
Intelligent question generation system with adversarial AI validation ensuring 100% mathematical accuracy.
- ●Multi-agent architecture with Code Executor, Logician, and Skeptic agents
- ●Parallel processing pipeline reducing validation time by 3x
- ●Zero-error mathematical verification through adversarial validation
Vehicle Class Detection System
TL;DR: Computer vision system that identifies different vehicle types from images with high accuracy
Achieved optimal model selection with comprehensive performance metrics
Deep learning pipeline comparing VGG16, InceptionV3, and ResNet50 with CUDA-accelerated training.
- ●Comparative analysis framework for multiple CNN architectures
- ●CUDA-optimized training pipeline for faster model iteration
- ●Production-ready model comparison and selection system
Causal Uplift Marketing Engine
TL;DR: AI system that targets customers based on "persuadability" rather than just purchase probability to maximize campaign efficiency
Achieved 0.0818 Qini Score, identifying the top 30% of users that drive maximum incremental revenue
End-to-end Causal Inference pipeline using Class Transformation (Lai Method) and XGBoost with custom hyperparameter tuning.
- ●Custom Qini curve visualization for robust causal model validation
- ●Interactive Streamlit dashboard for real-time customer segmentation
- ●Production-ready inference pipeline capable of identifying negative uplift ("Sleeping Dogs")
Autonomous CI/CD Healing Agent
TL;DR: An AI DevOps agent that finds bugs in your code, fixes them in a sandbox, and opens a PR—all while you sleep.
Reduces developer MTTR (Mean Time To Recovery) by automating the identification of syntax, logic, and dependency errors across Python and TypeScript.
A deterministic, multi-agent AI system using LangGraph to automatically detect, classify, and patch code failures with 100% containerized verification.
- ●8-Stage Deterministic State Machine: Orchestrated via LangGraph (Test → Classify → Plan → Apply → Commit → Wait for CI → Fetch → Verify).
- ●Isolated Execution Sandboxing: Uses Docker containers to validate AI-generated patches locally before pushing to production, eliminating hallucinations.
- ●Closed-Loop GitHub Integration: Automated workflow that clones repos, creates branches, triggers GitHub Actions, and opens Pull Requests for verified fixes.
- ●Real-Time Cyber-Dashboard: Modern React 19 interface providing live telemetry of the agent’s reasoning steps, score breakdowns, and diff summaries.
EduRAG: AI-Powered RAG Study Companion
TL;DR: An end-to-end Telegram bot utilizing a custom RAG pipeline to dynamically scrape syllabus PDFs and answer complex student queries instantly using advanced semantic search.
Transformed the student study workflow by reducing information retrieval time from minutes to seconds, delivering highly contextualized AI responses while completely eliminating hallucinated answers outside the curriculum.
Engineered a seamless Telegram-based AI bot utilizing a Retrieval-Augmented Generation (RAG) pipeline to automatically scrape, embed, and query university-specific study materials, providing instant, accurate answers grounded in the actual syllabus.
- ●Advanced RAG Architecture: Implemented an Ensemble Retriever combining BM25 (keyword search) and ChromaDB (semantic vector search via HuggingFace embeddings) for highly accurate information retrieval.
- ●Automated Data Pipeline: Built a custom web scraper using BeautifulSoup to dynamically locate, index, and download university subject PDFs directly from educational repositories.
- ●Telegram Native Interface: Developed a frictionless, chat-based UI using python-telegram-bot, allowing students to securely download notes and query LLMs without needing a standalone app.