AI Engineer

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Ravenna

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2024

☁️ SaaS

🤖 Artificial Intelligence

🏢 Enterprise

💰 $15M Seed on 2025-05

SaaS • Artificial Intelligence • Enterprise

Ravenna is an AI-powered internal service desk platform that lives inside Slack. It helps IT, HR, and Operations teams automate workflows, resolve employee requests, and deliver internal support using AI agents, visual workflow builder, analytics, and integrations with enterprise tools. The product emphasizes Slack-native UX, automation of routine requests (password resets, device provisioning, access requests), enterprise security and compliance (SOC2, HIPAA, GDPR), and is positioned as a SaaS solution for modern internal support.

📋 Description

• Build production AI systems: design and implement systems integrating LLMs into real product workflows. • Design reliable LLM architectures: develop robust systems, build safeguards and observability into features. • Develop evaluation and experimentation frameworks: create evaluation datasets and tooling for measuring model performance. • Build retrieval and knowledge systems: optimize retrieval pipelines powering LLM applications. • Ship high-quality product features: collaborate with product, design, and engineering teams for polished AI features. • Maintain strong engineering standards: write clean, maintainable, well-tested code and contribute to system architecture.

🎯 Requirements

• Strong engineering fundamentals: at least five years of experience building production systems. • LLM systems experience: experience building systems around large language models in production environments. • Evaluation mindset: experience in building evaluation frameworks, designing datasets, and improving system design. • Retrieval and embedding systems: experience with retrieval augmented generation systems, embeddings, and vector databases. • LLM fundamentals: understand how modern language models work at a conceptual level. • AI system intuition: familiarity with strengths and weaknesses of LLMs in practice. • Experimentation and iteration: comfort with running experiments across prompts, models, and system designs.

🏖️ Benefits

• Competitive Salary • Meaningful Equity • Choose Your Setup • Flexible Time Off