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Imad SidhoumIS

Imad Sidhoum

AI engineer / Data Scientist

600 €/jour
Paris, FR
3-7 ans

Délai de réponse moyen : 1h

À propos de Imad

Senior Machine Learning Engineer / Data Scientist with 4+ years of experience delivering production-grade ML and GenAI systems for large organizations (hospitality, media, large-scale data platforms).

I help companies move from POC to scalable AI products by designing end-to-end machine learning pipelines — from data ingestion and feature engineering to model deployment, monitoring, and continuous improvement in production.

My expertise includes:
• End-to-end ML pipelines (Python, SQL, PySpark)
• GenAI & RAG systems (LLMs, embeddings, retrieval, vector databases, evaluation)
• Production ML & MLOps (Docker, CI/CD, Airflow, MLflow, monitoring)
• Deep Learning (CNNs, Transformers, NLP, speech & audio pipelines)
• Cloud & Data platforms (AWS, Snowflake)

Recent projects:
• Production deployment of a customer churn prediction model for retention use cases in hospitality.
• Design and industrialization of RAG/LLM systems for internal knowledge platforms.
• Large-scale speech-to-text pipelines and retrieval systems over audiovisual archives.

I’m comfortable working with product, data, and business teams to turn complex problems into scalable, measurable AI solutions.

🎯 Available for: ML/AI engineering missions, GenAI/RAG projects, MLOps & productionization of models, end-to-end AI product delivery.
  • Français

    Bilingue ou natif

  • Anglais

    Capacité professionnelle complète

  • Arabe

    Bilingue ou natif

Accepte de travailler sur site
Paris (jusqu’à 50 km)

Expériences

  • Accor
    Senior ML Engineer / Data Scientist (Consultant)
    HÔTELLERIE
    juillet 2025 - Aujourd'hui (11 mois)
    Issy-les-Moulineaux, France
    • Contributing to an internal GenAI platform (knowledge base management, access control, evaluation frameworks).
    • Leading GenAI & RAG projects in production (LLMs, embeddings, retrieval, answer quality evaluation).
    • Designed and deployed a customer churn prediction model in production for hospitality retention use cases.
    • Built end-to-end ML pipelines: data ingestion, feature engineering, model training, evaluation, deployment, and monitoring.
    • Implemented model performance monitoring (data drift, seasonality effects) and continuous improvement loops.
    • Working closely with product, data, and business teams to turn ML outputs into actionable decisions.
    Churn prediction GenAI Retrieval-Augmented Generation (RAG) Model Deployment Agent IA
  • LittleBigCode
    Data Scientist Consultant
    CONSEIL & AUDIT
    juin 2023 - Aujourd'hui (3 ans)
    Paris, France
    • Designing and deploying production-grade ML and GenAI systems from data ingestion to monitoring.
    • Building RAG & LLM systems: embeddings, retrieval strategies, evaluation pipelines, and knowledge access governance.
    • Applied R&D on large-scale audio & speech data (STT pipelines with Whisper, detailed error analysis on noisy and heterogeneous audio).
    • Designing evaluation frameworks for embeddings, retrieval systems, and LLM-based applications in production.
    • Collaborating with product, data, and business teams to align AI systems with real-world use cases.
    • Documenting and presenting results to both technical and non-technical stakeholders.
    GenAI Retrieval-Augmented Generation (RAG) Speech-to-text LLM NLP
  • Atos
    Consultant Data Scientist
    CONSEIL & AUDIT
    septembre 2021 - juin 2023 (1 an et 9 mois)
    Bezons, France
    • Developed deep learning models for audio and video content analysis in production environments.
    • Worked on speech processing, audio event detection, and multi-label content classification.
    • Fine-tuned NLP and speech models for automatic content summarization and metadata enrichment.
    • Contributed to the transition from research prototypes to industrialized ML systems with engineering teams.
    • Prototyped ML/NLP models and contributed to data processing pipelines.
    • Participated in applied research projects across vision, audio, and NLP tasks.
    NLP Deep Learning LLM MLOps intelligence artificielle

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Formations

  • Generation (RAG)
    Generation (RAG)
  • Large Language Models (LLM)
    Large Language Models (LLM)

Compétences

Catégories