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Sekoya88/README.md

About

I architect production-grade multi-agent LLM pipelines, RAG systems, and serverless AI backends — then ship them to prod with the discipline of an athlete training 10-15h a week. Currently building cross-cloud AI solutions at Formalis.IA (AWS + GCP), automating complex legal document workflows at scale.


⚡ Current Focus

  • 🔧 Designing fault-tolerant agentic architectures (LangGraph, ReAct) with deterministic supervisors
  • 📚 Researching advanced RAG evaluation and retrieval ranking for domain-specific knowledge bases
  • 🤝 Open to collaborating on multi-agent systems, MLOps pipelines, and open-source LLM tooling

🛠 Tech Stack

Languages

Python TypeScript JavaScript SQL R Bash C/C++ Java LaTeX

ML / AI / Deep Learning

PyTorch TensorFlow HuggingFace scikit-learn OpenCV Pandas NumPy

LLM Ecosystem

LangChain LangGraph LlamaIndex vLLM Unsloth Ollama LoRA Vertex AI LangSmith

Backend & APIs

FastAPI Next.js Pydantic SQLAlchemy AsyncIO Redis WebSockets

Cloud & DevOps

Docker Kubernetes AWS Lambda AWS S3 AWS ECS AWS SAM GCP Terraform GitHub Actions

Databases

PostgreSQL pgvector Redis ChromaDB MongoDB Oracle DynamoDB

Architecture & Research

Clean Architecture DDD TDD Repository/Factory/Ports & Adapters Bayesian Inference Markov Chains Information Theory


🚀 Featured Projects

🛡️ Agentic Credit & Geopolitical Risk

Multi-agent LLM pipeline → CRO-level risk reports in <60s

3 autonomous agents (ReAct + LangGraph) with Redis checkpointing, ChromaDB RAG, and full LangSmith observability.

100% pipeline completion across 50+ test runs

LangGraph ChromaDB Redis Docker

📡 Multimodal Alpha Signal Extractor

Fine-tuned VLM on candlestick charts + NLP sentiment scoring for live trading signals

Custom pipeline combining vision-language models with real-time market data.

vLLM Unsloth LangChain

🌿 EcoChain

Sustainable ledger system integrating localized predictive analytics with robust backend infrastructure.

Python PostgreSQL Docker

⚡ PCBA Cost Prediction — Schneider Electric

End-to-end ML pipeline with Stacking Ensemble (XGBoost, RF, GBM, Ridge)

MAPE: 9.05% (down from 15.6%) · R²: 0.972

scikit-learn XGBoost Pandas


🏅 Education & Certifications

  • 🎓 Specialized Master in Big Data — GEM & Ensimag, Grenoble (2025–2026)
  • 🎓 Engineering (Electrical & Energy) — ESME, Lille (2022–2025)
  • 🎓 BA1 Mechanical Engineering — EPFL, Lausanne (2021–2022)
  • 📜 Stanford Machine Learning Specialization (2025)
  • 📜 Stanford Deep Learning Specialization (2025)

🏃 Beyond the Code

Same discipline. Different track.

🏃 Athletics 800m 1'55"77 · 1500m 3'58 · 3000m 8'48 · 5km 15'19 · 10km 33'20 · HM 1:14' · M 2:46'
🧗 Rock Climbing Since age 4 — translating route-reading into system design
🏉 Rugby 9 years competitive · international matches across Asia
🌏 Expatriate 3+ years in Bangkok, Thailand · fluent FR / EN / ES


Open to: Senior LLM Engineering roles · Agentic AI projects · Research collaborations

LinkedIn   Email

@Sekoya88's activity is private