Description

Architecting enterprise-scale AI systems for production.

With a strong academic foundation in Mathematics and Computer Science, I don't just integrate AI models; I design the robust, scalable systems that allow them to thrive in production.

My expertise lies at the rare intersection of Deep Tech engineering and Product-driven delivery.

🔄 A 10-Year Journey Through Tech Innovation

My career mirrors the evolution of the modern technological landscape. I have actively built and scaled systems through every major paradigm shift of the last decade:

The Data Era: Architecting Big Data and Modern Data Platforms.

The Infrastructure Shift: Embracing Cloud Native ecosystems, containerization (K8s), and distributed systems.

The AI Revolution: Moving from traditional Machine Learning and Deep Learning to the current frontier of Generative AI, LLMOps, and autonomous Multi-Agent Systems (SMA).

🛠️ The Dual Competence: Tech 🤝 Product

I believe the best architecture is the one that serves the user. I operate with a dual mindset:

Tech: Building resilient foundations (AWS, GCP, K8s, Lakehouse, Event-Driven Architectures) and mastering the math behind advanced AI paradigms (Agentic orchestration, memory, tool use).

Product & Delivery: Translating complex AI capabilities into interactive, ROI-driven data apps and APIs. I focus on Time-to-Market, UI/UX for data products, and measurable business impact.

🗣️ Cross-Functional Leadership

AI is a team sport. My core strength is communication: I speak the language of Data Scientists (math, algorithms), Software/DevOps Engineers (infrastructure, CI/CD, scalability), and Business Stakeholders (product roadmap, FinOps, KPIs). I align cross-functional teams to deliver enterprise-grade AI solutions without compromising on governance, observability, or sustainability.

Key Focus Areas: Agentic AI (SMA) | LLMOps & MLOps | Cloud Native Architecture | Tech-to-Product Strategy | Engineering Leadership

Industry field of expertise

Languages

French
Native or bilingual
English
Fluent
Arabic
Native or bilingual

Workplace preferences

Can work on-site

Paris (up to 50km)

EDF
Engineering manager - Data AI platform
ENERGY AND UTILITIES
April 2023 - Today (3 years and 2 months)
Paris, France
Mission
Directed cross-functional engineering teams (30+ engineers) to deliver 50+ SaaS, APIs, and workflows (SAFe agile).

Accomplishements :

• Defined and implemented AI/MLOps best practices : Versioning, tests, automation, monitoring, and reproducibility (fea-
tures store and models registry with AWS S3 Tables, real time tracking with mlflow).
• Led Research and Integration of New AI/data technologies and frameworks into workflows ensuring our solutions remain
cutting-edge (ex GenAI: Ollama code assist, Lakehouse, LLMOps, agentic design patterns).
• Created and promoted best practices, training programs to facilitate rapid adoption of AI technologies accross data and business units.

• Defined tech roadmaps for AI/Data initiatives.
artificial intelligence MLOps DataOps Data Platform Cloud computing
EDF
AI Data Platform Architect
ENERGY AND UTILITIES
January 2020 - April 2023 (3 years and 3 months)
Paris, France
Mission :
Led AI/Data platform design and build on OpenShift and AWS, enabling rapid delivery of production AI / Data applications (SAFe agile).

Accomplishments :

• Led data platform build with AWS S3 (storage), Airflow (orchestration), Kafka (streaming EDA), Redis (caching), DBT, and Gradio/Streamlit/FastApi
(APIs and interactive data apps).
• Delivered reusable 12-factor compliant blueprints that standardized software delivery across teams.
• Supervised DevOps implementation, with IaC (Terraform) and CI/CD pipelines (Jenkins, GitLab CI, Helm).
• Established observability stack (ELK, Prometheus, Grafana) to oversee the applications cycle, ensuring reliability and monitoring.

Result :

Our platform now hosts over 50 applications (web apps, jobs, APIs), with dev squads delivering agile and secure solutions to business units (Time to market divided by 9).

My Day to day tasks :

- Translate business requirements into secure, scalable, and reliable solution designs.
- Regularly develop proof of concepts and pilots, implement projects, and communicate new features and benefits to stakeholders.
- Document and share best practice knowledge for new solutions.
- Lead architectural design sessions and provide technical guidance throughout the project lifecycle.
Python MLOps Data Platform artificial intelligence AWS
EDF
Software Engineer - Data Analytics
ENERGY AND UTILITIES
October 2017 - January 2020 (2 years and 3 months)
Paris, France
Part of big data team, responsable of HADOOP Ecosystem.

Mission : My role involves designing and implementing big data pipelines to support real-time analytics and business intelligence initiatives.

Accomplishments :

- 360° Customer view : Create a coherent and up-to-date view of customer experience. Extract and transform data using Apache Spark, load into Apache Phoenix, a high-performance relational database layer over HBase for low-latency queries.

- Monitoring Application : Centralize logs of HADOOP processes into ElasticSearch (Build a module on top of log4j to integrate in oozie workflows), build a dashboard with KPIs (Kibana). Provided stakeholders with actionable insights for operational decision-making.

My Day to day tasks :

- Collaborate with cross-functional teams to define data requirements and architecture.
- Design and develop data pipelines (ELT) from HDFS to HIVE, HBase, Elastic using Apache Spark to transform data.
- Monitore data pipelines and manage incidents to ensure data integrity and availability.
Analytics Python Architecture distribuée Data Platform Data architecture