AI Engineer (EdTech)

TL;DR

AI Engineer (EdTech): Architecting and shipping agentic AI features for a collaborative learning platform with an accent on GenAI/LLM integration, RAG, and AI agent development. Focus on taking prototypes to production, optimizing for latency and costs, and scaling AI capabilities to handle usage spikes.

Location: Remote (Must be based in France)

Company

hirify.global provides a collaborative LMS that enables companies to upskill from within by leveraging internal experts instead of traditional top-down training.

What you will do

• Architect and ship the next generation of agentic AI systems from prototype to production.
• Own the technical strategy for AI features while collaborating with product, design, and engineering.
• Develop and optimize AI features such as conversational search, conversational BI, and AI course generation.
• Optimize production systems to reduce per-request costs, improve latency, and scale for usage spikes.
• Prototype and deploy advanced capabilities, including new modalities and real-time collaboration tools.

Requirements

• 2+ years of experience taking GenAI/LLM-powered systems from prototype to long-term production deployment.
• Strong Python experience with a track record of writing clean, maintainable production code.
• Hands-on experience with LLMs (Azure OpenAI, OpenAI, Anthropic, Mistral) and managing token costs, reliability, and hallucinations.
• Experience with RAG, semantic search, vector databases, and AI agents.
• Familiarity with LLM observability tools like Langfuse and model evaluation frameworks.
• English: B2 level or equivalent required.

Culture & Benefits

• Full remote work possible anywhere in France.
• Compensation package including base salary and equity.
• Practical perks: Work-from-home stipend, lunch vouchers, medical insurance, and gym subscription.
• Work-life balance: Flexible hours and 1 month parental leave for the second parent.
• Inclusive environment with active ERGs and a committed CSR Charter.

Hiring process

• Phone screen with the Talent Acquisition Manager.
• Discovery meeting with an AI Engineer.
• Case study and clarification meeting with an AI Engineer and Product Manager.
• Culture fit meeting with the VP of Engineering.