Ahmed Mostafa

My independent practice, helping companies get large language models into production. I work with engineering and product teams that have a real LLM problem and want it built by someone who has delivered this at scale — including work for clients like BNP Paribas, MSC, and Sky.

What I do:

RAG — searchable, cited answers over your own documents and data

Agents — systems that plan, use tools, and complete tasks end to end

Fine-tuning — domain- and language-specific models, cheaper and sharper on your work

MLOps/LLMOps — evaluation, monitoring, and safe releases so quality holds once real users arrive

Ahmed co-founded Pigro.ai in 2018 and served as Chief Technology Officer (CTO), leading both the technical development and AI engineering efforts of the company.

Pigro began in Italy as a chatbot engine for enterprises, built around a proprietary algorithm capable of extracting and understanding information from multiple documents and sources. Over time, the company evolved into a provider of enterprise search and retrieval-augmented generation (RAG) systems, helping organizations access and interact with internal knowledge through natural interfaces, including chatbots.

Pigro collaborated with major enterprises in Italy and Switzerland, including MSC, BNP Paribas, Sky, Amadori, and several Italian municipalities.

Key Responsibilities:

🔹 AI Research & Development Designed and implemented machine learning and deep learning models for a variety of natural language processing (NLP) tasks, including:

Automated Question Answering

Text Classification

Automated Question Generation

Text Summarization

🔹 Technical & Product Strategy Defined and executed the company’s engineering roadmap, ensuring scalable and efficient AI solutions aligned with business goals.

🔹 Business Development & Company Strategy Actively contributed to Pigro’s strategic direction by engaging with clients and investors, identifying market opportunities, and refining the company’s AI-driven value proposition.

🔹 Team Management Built and led a remote engineering team, fostering a collaborative culture focused on innovation, agility, and continuous learning.

Meedan is a nonprofit organization focused on building digital tools and community-driven programs that enhance the accessibility, reliability, and distribution of information, especially where and when it is needed most.

As a Machine Learning Engineer and Researcher at Meedan, he has contributed to several of the organization's open-source projects, including:

Check: A verification platform designed for fact-checking and collaborative investigations.

Alegre: A multilingual and multimodal similarity search engine for content analysis.

Presto: A serving framework for deploying AI models.

He developed machine learning models for text, image, audio, and video similarity, enabling sophisticated cross-modal content matching and verification.

In addition, he co-authored a peer-reviewed research article published in the International Journal of Public Opinion Research:

His work also includes contributions to natural language processing models, such as:

An embedding language model for Filipino and Tagalog:

A binary classifier for detecting whether input text relates to Brazilian elections: