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Senior AI Engineer | RAG · Multi-Agent Systems · LLM Ops | PhD · 6+ yrs | Open to work
I build AI systems that actually ship — RAG pipelines, multi-agent architectures, and LLM evaluation frameworks deployed on Azure at scale. 6+ years spanning Computer Vision research (PhD, SPIE/IEEE publications) and production Generative AI engineering. Currently Lead AI Engineer at Talan, where I designed a 3-agent RAG system for RQTH eligibility analysis — including a 3-layer security pipeline and a full RAGAS/Langfuse evaluation stack. Before that, delivered an MCP-based agentic platform for SNCF Voyageurs and led a portfolio of 5 GenAI projects across legal, insurance, and education sectors, managing 8+ engineers. I'm strong on both ends: research depth when the problem is novel, execution speed when it needs to ship. Stack: Python · LangChain · Azure OpenAI · FastAPI · Docker · FAISS · MLflow · Langfuse · RAGAS · pgvector · PyTorch Open to Senior/Lead AI Engineer, AI Architect, or Head of AI roles — Paris or remote. → mdalassaad@gmail.com Visit my Web site: https://mohamad-alassaad-github-io.onrender.com/
Université de Haute-Alsace Mulhouse-Colmar
Doctor of Philosophy - PhD, computer vision
January 1, 2019 – January 1, 2023
Lebanese University - Faculty of Engineering
Master 2 Research (TSCMI)
January 1, 2018 – January 1, 2019
Lebanese University - Faculty of Engineering
Engineering degree, Electrical and Electronics Engineering
January 1, 2014 – January 1, 2019
The Independents
Senior AI Engineer — GenAI Strategy & Adoption
April 1, 2026 – Present
Paris
Talan
Lead AI Engineer — Multi-Agent RAG System
December 1, 2025 – Present
Paris
SNCF Voyageurs
AI Engineer
October 1, 2025 – December 1, 2025
Greater Paris Metropolitan Region
Talan
AI Engineer & Researcher
September 1, 2024 – October 1, 2025
De Vinci Executive Education
Executive Lecturer - MBA Intelligence Artificielle et Data Innovation
September 1, 2024 – September 1, 2025
Université de Haute-Alsace - UHA
Teaching Assistant Professor
October 1, 2022 – August 1, 2024
Mulhouse
Université de Haute-Alsace Mulhouse-Colmar
Teaching Assistant
October 1, 2020 – September 1, 2022
Université de Haute-Alsace Mulhouse-Colmar
Researcher
October 1, 2019 – December 1, 2023
Laboratoire IBISC - CNRS
Internship: SDN and NFV
March 1, 2019 – July 1, 2019
Évry, Île-de-France, France
Neotic
Machine learning Developer
July 1, 2018 – August 1, 2018
Lebanon
Ericsson
R&D Intern
July 1, 2017 – August 1, 2017
Beirut-Lebanon
TensorFlow: Data and Deployment Specialization
Coursera
June 25, 2026 – Present
Professional Scrum Master I
Scrum.org
June 25, 2026 – Present
Deep Learning Specialization
Coursera
June 25, 2026 – Present
AI for Medicine Specialization
Coursera
June 25, 2026 – Present
Natural Language Processing Specialization
Coursera
June 25, 2026 – Present
DeepLearning.AI TensorFlow Developer Specialization
DeepLearning.AI
June 25, 2026 – Present
Robotics: Perception
University of Pennsylvania
June 25, 2026 – Present
Microsoft Certified: Azure AI Engineer Associate
Microsoft
June 25, 2026 – Present
Machine Learning Engineering for Production (MLOps) Specialization
Coursera
June 25, 2026 – Present
TensorFlow: Advanced Techniques Specialization
Coursera
June 25, 2026 – Present
Deep Neural Networks with PyTorch
IBM
June 25, 2026 – Present
Cultural Fit Analysis
The candidate's background includes both deep academic research and practical industry application across various sectors (luxury PR, HR, transportation, legal, insurance). This blend suggests a strong drive for innovation and problem-solving, which aligns well with a forward-thinking technical culture. Their experience in leading teams and consulting on AI strategy indicates a proactive and influential approach, contributing positively to cultural fit.
Soft Skills & Operational Fit
The candidate's experience as a Scrum Master (PSM I certification) and in facilitating business workshops suggests strong organizational, leadership, and communication skills. Their teaching roles further highlight an ability to explain complex technical concepts. The diverse project portfolio indicates adaptability and a collaborative mindset, crucial for operational fit in dynamic ML engineering environments.