Freelancer profile translated to English.

Description

Holder of a PhD in Computational Physics and with over 7 years of experience, Michel MAMA TOULOU specializes in Data and AI - Artificial Intelligence project management, with recognized expertise in industrializing artificial intelligence algorithms and digital transformation.

Michel MAMA TOULOU excels in implementing data ingestion processes, building and planning data pipelines, orchestration, automation, and deployment, with the implementation of a modern medallion architecture integrating bronze, silver, and gold layers. He has successfully led projects where data integration and production exploitation were at the core of the challenges, while ensuring the sustainability of developments through agile processes and continuous integration, deployment, and delivery (CI/CD).

Mastring areas such as data ingestion and transformation, cloud migration, MLOps, predictive model development, predictive maintenance, and data governance, Michel has collaborated successfully in various sectors, including automotive, aeronautics, metallurgy, and consulting.

Since the beginning of his career, Michel MAMA TOULOU has demonstrated strong leadership in structuring R&D teams, contributing to measurable impacts on operational performance and organizational profitability. His skills and strategic sense make him a key asset for ambitious and innovative projects.

Industry field of expertise

Languages

French
Native or bilingual
English
Native or bilingual

Workplace preferences

Can work on-site

Paris (up to 50km)

Agrial
Fabric Data Engineer
AGRICULTURE
May 2025 - July 2025 (2 months)
As part of evaluating Microsoft Fabric's capabilities against existing Power BI solutions, I led the design of two complete analytics pipelines (ingestion, transformation, modeling, visualization) using Agrial's CCH Tagetik data (15 GB, 90 million rows).

Key Achievements:

Data Architecture:Design of two complete analytics chains (ingestion, transformation, reporting) with Power BI (Dataflow Gen1, Import mode) and Microsoft Fabric (Data Factory, Lakehouse, Warehouse, Direct Lake).

Ingestion & Performance:Extraction of 15 GB (90 million rows) from SQL Server via Gateway. Ingestion time reduced to 7 minutes with Fabric (vs. 1h31 with Power BI).

Transformation:Development of complex business logic (pivot, currency conversion, N-1 calculations), optimized via T-SQL in Fabric Warehouse.

Reporting & Modeling:Creation of Power BI reports with semantic models (Import & Direct Lake). Near real-time performance in Direct Lake with real-time access.

Governance & CI/CD:RLS security on Fabric Warehouse, integration with Git (Azure DevOps).

Technical Comparison:
Fabric demonstrated significant gains in performance and capacity consumption (P1/F64), with near real-time access via Direct Lake.

Technical Environment:Microsoft Fabric, Power BI, SQL Server, T-SQL, DAX, Git, Azure DevOps.
axa
Lead AI Engineer
BANKING AND INSURANCE
March 2025 - Today (1 year and 3 months)
Implementation of LLM Guardrails for an Intelligent Assistance Application based on Azure OpenAI

Summary:
Development and industrialization of a security framework (guardrails) for a conversational agent based on Azure OpenAI GPT-4o. The objective was to ensure the safety, compliance, relevance, and performance of responses generated by the LLM in a sensitive context (insurance, contracts, customer data).

Development of LLM Guardrails:
- Creation of custom validators for **content filtering, keyword filtering, language compliance, chunk relevance, prompt injection**, and prompt leakage prevention.
- Implementation of aRAGpipeline withAzure AI Searchfor indexing and vector search of business documents.

Infrastructure and Deployment on Azure:
- Use ofAzure Functionsto expose LLM endpoints via a serverless architecture.
- Integration withAzure CosmosDBto store conversation histories, logs, and metadata.
- Implementation ofAzure Cache for Redisfor caching frequent responses and optimizing latency.
- Real-time monitoring viaAzure Application Insightsfor collecting metrics, errors, and alerts.

CI/CD & DevOps:
- Source code management via Azure Repos.
- Implementation of automatedCI/CDpipelines withAzure Pipelinesfor build, unit tests, release, and deployment of cloud functions and RAG components.

Use of the Azure AI Foundry Suite:
- Integration of Azure OpenAI GPT-4o,Azure Content Safetyfor moderation, and other services from the AI Foundry suite to accelerate responsible LLM development.

SDK Development and User Interface:
- Design of an interactive front-end withChainlitfor testing, internal demonstrations, and functional validation by business users.
EDF
Data Engineer
ENERGY AND UTILITIES
July 2025 - September 2025 (2 months)
Context:
Implementation of a Microsoft Fabric POC for EDF.

This includes:
- Migration of AAS (Azure Analysis Service) cubes to Microsoft Fabric
- Documentation of the migration from Power BI Premium capacity to Fabric capacity
- Connecting to and collecting data from BigQuery using Fabric.
- Implementing a Medallion Architecture including the deployment of a lakehouse with three schemas: bronze for raw data, silver for processed data, and gold for business-ready data to feed Power BI reports.
- Development of machine learning workloads in Fabric notebooks, including setting up an environment configured with specific dependencies.
- Implementing Direct Lake mode to feed Power BI reports that consume data directly from the source.
- Activating a versioning process in dev, test, and prod workspaces.
- Connecting workspaces to Azure DevOps for versioning and CI/CD.
- Configuring the Deployment Pipeline.
- Managing security at the tenant, workspace, and Fabric element levels.

Deliverables:
- Staff training
- Action development
- Documentation delivery
Microsoft Fabric Gitlab CI/CD