Checking whether a new connection was feasible required several tools: finding the best route, checking available capacity, simulating the signal and verifying equipment. The analysis could take several hours.
Available from September 2026 France, open to international opportunities
AI Engineer, agents and generative AI
I build AI tools that work in the real world.
An ESIEE Paris engineering graduate, I enjoy starting with a concrete problem and building the right tool for it. My work combines generative AI, business data and software development.
End to end
from a question to a technical report
2,172
optical services modelled
< 6 s
median latency
My focus
AI agents→Business tools
Case study at Orange Innovation
Andiamo Agent
A conversational AI assistant for operating and planning a long-haul optical network, the infrastructure that carries data between cities and countries.
A LangGraph orchestration engine coordinates several specialized AI agents. They query network data, calculate routes and use GNPy, software that simulates optical signal quality, before producing a shareable report.
1 request
to go from a question to a complete technical report
< 6 s
to produce an actionable answer
2,172
network services accessible in natural language
4 sections
route, capacity, equipment and signal quality
Demos
A few examples on video.
Short extracts from a demonstration environment. All visible data has been anonymized.
01 / Network map
Explore the topology
Interactive view of the links, regional filters and remaining capacity on each connection.
02 / AI assistant
Query the network
A business question written in natural language is routed to the right agents and data sources.
03 / End-to-end workflow
Run a feasibility study
Route selection, equipment choice, signal-quality simulation and generation of a decision report.
Generated report
The final decision, presented in a document teams can use.
The report brings together the selected route, available capacity, required equipment and the signal-quality simulation.
Architecture
How it works.
The router selects the appropriate agent. Each agent has its own tools and data sources. A quality judge checks the response before it is returned.
From a business request to a verified result
01
→ Business question
Written naturally by the user
02
→ Agents and tools
Network data Route calculation GNPy simulation
03
Verified response
Clear decision and detailed report
About
What interests me about AI.
I recently graduated from ESIEE Paris in Data Science and AI. I am particularly interested in AI agents: specialized assistants that work together and use tools.
At Orange Innovation, I learned to put these ideas to work in a real environment, with imperfect data, technical constraints and users who need clear answers.
Product
Start with the business need, not the model.
Reliability
Measure, trace and validate responses.
Systems
Connect AI models to real tools and data.
Career
Experience
My experience spans research, product development and client-facing work.
02/2026 to present
Final-year internship
01Orange Innovation
AI Engineer for optical network management
- Designed a multi-agent assistant to analyze and simulate a long-haul optical network from questions written in natural language.
- Connected the AI system to network maps, available equipment and active services through GraphQL, PostgreSQL and Python tools.
- Validated the full workflow, from the user’s question to a report detailing the route, capacity, equipment and signal quality.
05/2025 to 08/2025
Research internship
02- Compared sociological biases and behavior across five language models, including LLaMA and GPT, on human and societal topics.
- Designed a LangGraph multi-agent workflow to analyze around thirty interviews using the Braun and Clarke thematic analysis method and the Groq API.
- Added inter-agent validation and consistency checks to improve the reliability of thematic coding.
01/2025 to 02/2026
03- Managed more than five client engagements on technology topics, from requirements analysis to final delivery.
- Managed budgets ranging from €1,000 to €20,000 depending on project complexity.
- Prepared business proposals and client presentations while coordinating teams of student consultants.
Skills
The tools I use.
I mainly work with AI agents, data and software development tools.
AI agents and generative AI
01Build assistants that collaborate, use tools and verify their answers.
LangGraphLangChainMCPRAGRequest routingTool-using agents
Software and data
02Connect AI systems to databases and reliable services.
PythonFastAPIGraphQLPostgreSQLRedisSQL
Machine learning
03Train, compare and evaluate models with a rigorous approach.
PyTorchTensorFlowScikit-learnXGBoostPandasEvaluation
Production
04Turn a prototype into a monitored, tested and deployable application.
DockerKubernetesCI/CDGitLinuxMonitoring
In simple terms: an LLM understands and generates text; RAG helps it retrieve reliable information; MCP provides a standard way to use external tools.
Selected work
Other projects
Kaggle project, 04/2025 to 05/2025
Building energy consumption prediction
Data analysis, feature engineering, rigorous comparison of several models and fine-tuning of the two best performers.
PythonXGBoostRandom ForestPandasScikit-learn
Academic project, 11/2024 to 12/2024
Data collection, storage and visualization pipeline
An end-to-end application that collects web data, stores it, speeds up queries, monitors quality and displays the results in a dashboard.
PythonScrapyMongoDBRedisKibanaDash
Contact
Working on an AI project?
Let’s talk.
I am looking for an AI Engineer or Generative AI Engineer position from September 2026. I am mobile across France and open to international opportunities.