Specialized in developing cutting-edge ML/DL architectures with 5+ years of experience. From computer vision to NLP, I architect solutions that solve real-world problems at scale.
Hi, I’m Carlo Calledda. I started with a thesis on neural networks for seismic design and academic research in data science and machine learning engineering, which I turned into specialized experience developing end‑to‑end MLOps solutions and computer vision systems.
Today I’m a Data Scientist specialized in MLOps implementations and microservice systems: I design automated ML pipelines, build REST APIs with FastAPI, and deliver containerized deployments. I have proven experience delivering end‑to‑end solutions, from dataset automation tools to production‑ready services.
I have a solid foundation in structural engineering and growing expertise in NLP and large language model applications, with a strong passion for Rust. I aspire to lead projects that combine my MLOps background with emerging ML/NLP/LLM technologies in companies that value technical excellence, automation, and impactful AI solutions.
A Model Context Protocol (MCP) server that transforms Rust documentation into a queryable knowledge graph, enabling semantic search and AI‑assisted discovery. (Updated 3 weeks ago)
MCP server for parsing and querying Rust documentation. Extracts details about functions, structs, and enums, exposing them via a programmable interface. (MIT, updated on Jun 15)
MCP server that analyzes Python projects, extracts library signatures, and surfaces project information to help AI assistants understand codebases. (MIT, updated on May 18)
The "Prediction" project: a high‑performance object detection service built with Rust and Actix‑Web, focused on robustness and production‑ready APIs. (Updated on Feb 16)
A lightweight AI agent orchestrator built in Rust. Features a high-performance Actix-web backend for agent lifecycle management (create, run, pause, cancel) and a real-time WASM dashboard for monitoring. (Updated 4 days ago)
Reinforcement Learning trainer for LLM function calling using GRPO. Features AST-based reward functions, QLoRA quantization optimized for consumer GPUs, and xLAM dataset support. (Updated last week)
High-performance Rust implementation of GraphRAG (Graph-based Retrieval Augmented Generation) that builds knowledge graphs from documents and enables natural language queries. ⭐ 16 stars (Updated 2 weeks ago)
A fast, lightweight tool to parse Rust projects into a knowledge graph and run insightful queries over code structure. Generate DOT/SVG visualizations, persist graphs to JSON, and explore code relationships. (Updated on Aug 22)
WebAssembly-based chat application built with Rust, featuring local LLM execution in the browser. Privacy-focused architecture with client-side inference. (Updated on Jun 28)