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Mohammed Shabbir AliMS

Mohammed Shabbir Ali

Freelance AI & ML Engineer

€645/day
Paris, FR
8-15 years

Average response time: 1 hour

About Mohammed Shabbir

You have documents, data, or video — you need a working AI system with proof that it works. That's what I deliver.

I help companies ship production-ready AI in weeks, not quarters:

🔹 GenAI & RAG applications — chatbots and Q&A over your documents, with citation-backed answers your teams can trust (LangChain, ChromaDB, local or hosted LLMs)

🔹 LLM fine-tuning & LLMOps — custom models on your data (LoRA/QLoRA), with evaluation pipelines and regression gates so every release is measurably better, never worse

🔹 Computer vision — object detection, tracking and video analytics, deployed to cloud or edge devices (YOLO, Jetson, real-time)

🔹 Anomaly & fraud detection — graph-based ML for spotting what doesn't belong in your transactions, networks or sensor data

What makes me different: PhD (Télécom Paris) and 10+ years turning ambiguous problems into deployed systems. Every project ships with evaluation reports, dashboards and reproducible code — I don't deliver models, I deliver models with evidence.

Typical engagements: scoped pilot (2–3 weeks) → measured results → full build. Remote, available now.

Core stack: Python, PyTorch, Transformers, PEFT/LoRA, LangChain, ChromaDB, Ollama, scikit-learn, OpenCV, Docker, GitHub Actions, Streamlit.
  • English

    Native or bilingual

  • French

    Conversational

  • Arabic

    Basic

  • Hindi

    Native or bilingual

  • Urdu

    Native or bilingual

Remote only
Primarily works remotely

Experience

  • ZEMOTECH
    Freelance AI & ML Engineer
    RESEARCH
    May 2024 - Today (2 years and 2 months)
    Paris, France
    ○ Independent AI/ML practice delivering GenAI, computer-vision, and evaluation systems to R&D clients end to end — scoping,
    build, evaluation, and hand-off.
    ○ Built and shipped public and client AI projects across RAG, QLoRA fine-tuning, LLMOps regression gates, and validated NLP screening (see AI Highlights above).
    ○ Developed a graph-attention-network (GNN) anomaly / spoof detector on a 120,798-sample adversarial dataset, achieving
    PR-AUC 0.9401 and F10.9070 under strict leave-one-attack-out evaluation — transferable to fraud, intrusion, and security
    analytics.
    ○ Delivered a 14-month R&D contract (VEDECOM) building hybrid test/evaluation frameworks and real-time monitoring dashboards
    Intelligence artificielle (IA) / Artificial intelligence LLM, RAG, Machine Learning, Computer Vision, MLOps Machine learning Computer Vision NLP
  • VEDECOM Institute
    R&D AI Engineer
    TELECOMMUNICATIONS
    June 2019 - December 2024 (5 years and 6 months)
    78000 Versailles, France
    ○ Developed a receiver-side misbehavior detection approach for V2X / CPM data using vehicle perception context, published at IEEE VTC Fall 2023 2 Online .
    ○ Built machine-learning regression and clustering models to estimate vehicle speed and distinguish pedestrians, bicycles, and vehicles from Bluetooth sensor and ground-truth data.
    ○ Created Bokeh-based analytics dashboards and KPI pipelines for real-time monitoring, daily and weekly traffic patterns, and congestion analysis from roadside sensing data.
    ○ Contributed to cooperative perception simulation and V2X integration work using OMNeT++, Veins, SUMO, CPM/CAM processing,
    and scenario-based evaluation.
    C++ Computer Vision Python GPU deeplearning
  • Floware
    Chief Technology Officer
    TRANSPORTATION
    March 2022 - March 2023 (1 year)
    Paris, France
    ○ Led technical development of a roadside traffic-sensing platform combining computer vision and Bluetooth-based analytics for
    transport monitoring.
    ○ Deployed YOLO v4-v7 object detectors and SORT, DeepSORT, and OCSORT trackers on Nvidia Jetson Nano, Raspberry Pi, and Google Coral TPU for real-time transport-mode detection and tracking.
    ○ Developed track-recovery and post-processing algorithms that improved counting and tracking accuracy by 95% against
    manually prepared ground truth, delivering the end-to-end pipeline from edge inference to time-series KPIs.
    Computer Vision GPU Data science Python C++

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Education

  • PhD in Computer Science and Networks
    ParisTech
    2017
    Computer Science and Networks

Certifications

  • PhD Certificate
    Telecom Paris
    2017

Skill set

Categories