AI Product Engineer

Speak; Deutschland; Remote; EUR 65.000 - 90.000; Vollzeit

Zusammenfassung

A language learning company in Germany is looking for a Full Stack Software Engineer to develop and deploy LLM-powered products. The ideal candidate will have 3-5+ years of experience in software engineering, a strong focus on LLM applications, and the ability to work collaboratively across teams. This role offers the chance to enhance existing AI features and scale products for a global audience.

Qualifikationen

• 3-5+ years of experience in full stack/backend software engineering.
• Proven experience with LLM applications and optimization.

Aufgaben

• Develop and deploy LLM-powered language learning products.
• Collaborate with cross-functional teams on product features.
• Refine processes for building LLM applications.
• Scale product features for users and languages.
• Ensure quality assurance of LLM systems.

Kenntnisse

Software engineering

LLM applications

LLM Ops

Performance optimization

Cross-functional collaboration

Tools

Vector databases

Prompt ops

Jobbeschreibung

What we're looking for?

We need someone with 3-5+ years of experience in full stack/backend, product-focused software engineering who has real-world experience developing and deploying LLM applications. You should be comfortable working autonomously and building at high velocity, with a keen intuition for improving performance and output quality of LLM systems. A strong understanding of LLM Ops and tools (e.g., vector databases, RAG, prompt ops) is essential. Bonus points if you have a mix of big tech and startup experience, demonstrating the ability to work with large datasets and thrive in a high-agency environment.

What you'll do?

• Develop and deploy LLM-powered language learning products across the full stack, and enhance the quality and performance of existing AI-powered features within Speak.
• Collaborate cross-functionally with other Engineering teams, Applied ML, Product, Design, and Content to build and scale novel product features.
• Refine our process for building LLM apps, including best practices for prompting, experimentation/evaluation, LLM Ops, and measuring quality and performance.
• Scale existing product features to many more users and languages, ensuring an exceptional and unparalleled language learning experience.
• Take ownership of the implementation and quality assurance of LLM systems, ensuring internal benchmarks are met.