Directed a team in creating a legal AI assistant using multi-agent architecture (Autogen), enhancing legal research efficiency for an Italian law firm.

Engineered a Python-based data pipeline for complex legal datasets, processing over 80GB into Weaviate and developing LLM-generated schemas to improve RAG accessibility.

Developed an AI-powered survey management system, automating analysis and reducing survey data processing time.

Instituted a data warehouse and managed FastAPI and Go/MongoDB back-end integrations for AI services.

Enhanced AI talent search for a SaaS platform using Generative AI and RAG, incorporating an SVM CV classifier and semantic resume ranking.

Designed an Agentic AI chatbot (LangGraph, Autogen) with a GPT-4 function calling and multi-channel integration, streamlining engineering team access to AI tools and boosting internal productivity.

Implemented Langfuse for comprehensive AI interaction tracing and Redis caching, optimizing system performance and contributing to a notable reduction in API operational costs.

Led a team in developing ML models for ETA prediction on the Zmall platform, achieving a 10% reduction in operational costs.

Managed back-end refactoring and API development for the "Zmall" delivery system, significantly lowering operator action latency and improving system scalability.

Devised a personalized recommendation engine using Python and Spark, resulting in a 10-fold increase in sales and a 30% lift in user engagement.

Developed and maintained modules using Python and Odoo ERP, enabling customers to ease their work.

Built an API that helped in data gathering, cleaning, and preparation for analytics, improving productivity.

Led a team of 3 to develop a data migration automation system using an Odoo ERP, which improved data migration time.

Optimized database queries by code refactoring, which led to performance improvement.

Improved targeted marketing by performing data analytics of sales and customer data and helped to direct business plans.