Professional Overview
I'm an AI Automation Engineer specializing in n8n workflows, LLM agent integration, and API/CRM automation. I run my own agency, taking systems from client scoping to production — self-hosted infrastructure, GPT-4o/Claude integrations, and reliable, maintainable pipelines. Native French, fluent English and Spanish.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Build and deploy n8n workflows with OpenAI/Claude APIs and CRM integrations
- Scope automation needs with clients and design the system architecture
- Self-host and administer infrastructure: Docker, Nginx, SSL, monitoring
- Handle error workflows, edge cases and production breakages
- Package automations as products: setup fee plus monthly subscription
Key Achievements / KPIs
- 10 AI systems built, 6 delivered and running in client production
- Recurring retainers of EUR 200-500/month per client
- Lead response time cut from hours to under 30 seconds
- 250,000+ customs records processed to rank 4,900 categories into 14
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Build and deploy n8n workflows with OpenAI/Claude APIs and CRM integrations
- Scope automation needs with clients and design the system architecture
- Self-host and administer infrastructure: Docker, Nginx, SSL, monitoring
- Handle error workflows, edge cases and production breakages
- Package automations as products: setup fee plus monthly subscription
Key Achievements / KPIs
- 10 AI systems built, 6 delivered and running in client production
- Recurring retainers of EUR 200-500/month per client
- Lead response time cut from hours to under 30 seconds
- 250,000+ customs records processed to rank 4,900 categories into 14
Areas of Expertise
Automation & AI Workflows
Build intelligent automation pipelines, integrate AI APIs into existing tools, and replace manual workflows with agentic AI systems.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
