Data Scientist / AI Engineer (SaaS)

TL;DR

Data Scientist / AI Engineer (SaaS): Building and operating LLM-based systems and machine learning models to drive product innovation with an accent on prompt engineering, RAG pipelines, and model evaluation frameworks. Focus on shipping production-ready AI use cases, managing GPU infrastructure, and collaborating with C-suite stakeholders to impact business metrics.

Location: Must be based in or able to work from Paris (Hybrid or Remote within France)

Company

A profitable, bootstrapped B2B SaaS platform valued at $150M that helps 40,000+ sales teams worldwide close deals more effectively.

What you will do

• Identify and implement AI use cases with direct business impact.
• Design, develop, and maintain LLM-based systems including orchestration and retrieval pipelines.
• Deploy and operate AI models and infrastructure on cloud platforms.
• Train, evaluate, and maintain machine learning models in production environments.
• Build and improve evaluation and observability frameworks for AI systems.
• Provide data-driven insights to product and leadership teams.

Requirements

• 4+ years of experience in Data Science or AI Engineering.
• Advanced proficiency in Python and SQL.
• Hands-on experience with LLM systems, agentic pipelines, and RAG.
• Experience with agent frameworks like LangChain and deep learning libraries like PyTorch.
• Experience deploying models on cloud infrastructure, specifically GCP.
• Must be fluent in French and English.

Nice to have

• Experience with workflow orchestration tools like Airflow.
• Knowledge of GPU server management for model training.
• Background in B2B SaaS startup environments.

Culture & Benefits

• Competitive salary with annual company performance bonus up to 18k€.
• 38 days of paid holidays per year.
• Comprehensive 100% premium medical coverage via Alan Blue.
• Daily meal tickets via Swile and covered Navigo commuting card.
• Annual team building retreats in international locations.
• Provision of high-quality laptop and necessary equipment.

Hiring process

• Initial call with Talent Acquisition and AI Engineer.
• Live technical interview with engineering team.
• Interview with CEO.
• Reference checks and offer.