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.

Industry field of expertise

Languages

English
Native or bilingual
French
Native or bilingual

Workplace preferences

Can work on-site

Zurich (up to 50km), Lausanne (up to 50km), Genève (up to 50km)

Datadog
Machine Learning Engineer
SOFTWARE PUBLISHING
January 2024 - April 2026 (2 years and 3 months)
Zurich, Switzerland
- 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
TECH
May 2022 - January 2024 (1 year and 8 months)
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
REAL ESTATE
May 2020 - May 2022 (2 years)
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