Description

github.com/lbougan

I design and build production-grade AI systems, from LLM-powered pipelines and RAG architectures to scalable ML infrastructure on AWS and GCP.

With 6 years of experience spanning ML engineering, cloud infrastructure, and data engineering, I've built multi-tenant ML platforms processing 200GB+/day, deployed NLP and computer vision models end-to-end, and developed LLM-based data integration pipelines for complex real-world use cases at Swiss & US companies.

I help teams go from prototype to production : fast, clean, and reliable.

Domaines d’expertise

Langues

Anglais
Bilingue ou natif
Français
Bilingue ou natif

Préférences en matière de lieu de travail

Accepte de travailler sur site

Zurich (jusqu’à 50 km), Lausanne (jusqu’à 50 km), Genève (jusqu’à 50 km)

Datadog
Machine Learning Engineer
EDITION DE LOGICIELS
janvier 2024 - avril 2026 (2 ans et 3 mois)
Zurich, Suisse
- Building and operating LLM-powered agentic systems that automate incident triage, root cause analysis, and security remediation across Datadog's observability platform.
- Designing retrieval pipelines (RAG) over telemetry data (logs, traces, metrics) to power Bits AI's contextual assistant.
- Developing evaluation harnesses, guardrails, and feedback loops to ensure model reliability and safety in production-grade agentic workflows.
- Shipping end-to-end ML inference services, from containerized model deployment to GPU-accelerated serving, with proper monitoring, alerting, and observability baked in.
- Building and maintaining MCP integrations enabling external LLM agents to interact programmatically with Datadog's platform.
Tech: Python, Go, PyTorch, Kubernetes, Docker, Ray, AWS/GCP, LangChain/LangGraph, MLflow, Datadog.
Python Cloud computing Pytorch Datadog LangGraph
Arch Systems
Senior AI / ML Engineer
HIGH TECH
mai 2022 - janvier 2024 (1 an et 8 mois)
Led the design, development, deployment, and monitoring of AI driven features used by global manufacturing clients.
- Built ML-powered audio transcription systems.
- Designed data labeling and preprocessing pipelines for supervised learning tasks.
- Enhanced data reliability and observability for ML experimentation.
- Improved ELT workflows supporting ML training datasets (>150 total data points/s).
Tech stack: Python, PyTorch, Django, React, SQL, dbt, AWS, Kubernetes, Docker.
Python Pytorch Cloud computing Machine learning Data Engineering
MoneyParkAG
Software Engineer
IMMOBILIER
mai 2020 - mai 2022 (2 ans)
Zürich, Switzerland
Built core systems for a high-traffic real estate and mortgage platform.
- Owned development of quantitative tools and asynchronous services for real estate analytics.
- Maintained key backend services generating millions in revenue.
- Developed the backend and frontend components of the real estate platform from zero.
Tech stack: Python, Django, TypeScript, Node.js, React, GCP, Docker, Kubernetes.
Python Cloud computing Django Typescript Google cloud