Freelancer profile translated to English.
Back to original languageAbout Mohamed Aniss
Expert in designing and deploying robust, scalable, and high-performing solutions around LLMs and AI in general, I assist companies in industrializing their generative AI projects. My approach is resolutely focused on system architecture, infrastructure optimization, and the creation of reliable end-to-end autonomous architectures.
My Areas of Expertise
- Autonomous Systems and Agents:I design and deploy intelligent agents based on reliable, highly structured, and production-ready architectures. I master the management of their execution environment, the integration and calling of complex tools (tool use), as well as the implementation of advanced short-term and long-term memory management mechanisms.
- Advanced RAG Pipelines:I have designed and put into production advanced RAG architectures. My systems integrate custom chunking and preprocessing strategies for complex documents, multi-stage filtering, and reranking mechanisms to maximize the relevance of extracted data.
- Model Fine-Tuning & Evaluation:I master the entire LLM training lifecycle. I am involved from the creation and curation of complex datasets to the implementation of rigorous evaluation pipelines on public and custom benchmarks.
- Distillation, Compression & CPU/GPU Deployment:To reduce infrastructure costs and secure data, I develop model distillation and compression systems (quantization, LoRA). I deploy these models and agents on robust and optimized architectures, suitable for high-performance inference on both CPUs (local computing) and GPUs (distributed environments).
Arabic
Native or bilingual
French
Native or bilingual
English
Fluent
Can work on-site
Paris (up to 50km)
Experience
- Data Impact by NielsenIQData ScientistE-COMMERCEFebruary 2022 - October 2022 (8 months)Paris, FranceDuring my assignment, I focused on improving the company's existing machine learning solutions by leveraging the latest state-of-the-art advancements, where I:
- Proposed a new model based on temporal embeddings and the Transformers architecture for predicting customer market share on amazon.com.
- Worked on fine-tuning multilingual models XLM and mBert for multilingual sentiment analysis using an English review base and employing zero-shot learning for other languages.
- Worked on containerizing models using Docker and FastAPI.
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Education
- Master's degree, Machine learning for data scienceUniversity of Paris2022Master's degree, Machine learning for data science
- Master's degree, Distributed Artificial IntelligenceUniversité Paris DESCARTES2021Master's degree, Distributed Artificial Intelligence
Certifications
- AWS Certified Solutions Architect – AssociateAmazon Web Services Training and Certification2023