Applied AI Engineer (f/m/x)

About the Role

As part of the engineering team, the Applied AI Engineer will collaborate closely with Product, DevOps, and customer-facing teams to turn requirements into high-quality software. This involves working on RAG pipelines, retrieval strategies, and LLM integrations, making pragmatic trade-offs across quality, latency, and cost. The team at MAIA values strong ownership mentality, people skills, and the ability to explore and apply AI in innovative ways.

MAIA's goal is ambitious - to become the leading AI knowledge management platform for industrial companies in Europe. To achieve this, the company is looking for individuals who can contribute to building and evolving its core product capabilities, with a focus on backend and AI systems.

What You Will Do

• Build and evolve MAIA's core product capabilities, focusing on backend and AI systems.
• Design, implement, and ship features from discovery to production rollout, with a strong focus on backend and AI functionality.
• Develop RAG pipelines, retrieval strategies, and LLM integrations that work reliably in production.
• Make pragmatic trade-offs across quality, latency, and cost for RAG pipelines and LLM integrations.
• Build and iterate on evaluation approaches for LLM features, including quality gates, regression detection, and observability.
• Collaborate closely with Product, DevOps, and customer-facing teams to turn requirements into high-quality, maintainable software.
• Proactively scout new technologies and approaches, translating findings into production-ready solutions.
• Push the team's understanding of what is possible with AI and backend systems.
• Work on building and operating LLM applications, ideally with exposure to RAG systems and retrieval concepts.
• Contribute to the development of commercial LLM frameworks and vector databases.
• Debug production systems using observability tooling like logs, metrics, and tracing.

What We Are Looking For

• Strong backend engineering skills in TypeScript, or another strongly typed language, with solid TypeScript fundamentals.
• Proven experience building and shipping production REST APIs.
• Strong SQL skills, ideally with PostgreSQL in production.
• Hands-on experience building or operating LLM applications, with exposure to RAG systems and retrieval concepts.
• Commercial experience with at least one relevant LLM framework and at least one vector database.
• Practical experience debugging production systems using observability tooling.
• Foundational understanding of cloud and DevOps concepts, enough to collaborate effectively with a dedicated DevOps engineer.
• Ability to follow secure development best practices in day-to-day engineering work.
• Fluency in English; German is a plus.
• Strong ownership mentality and people skills.
• Ability to experiment with models, prompts, agents, and workflows, with informed opinions about what works.

Nice to Have

• Experience with Supabase.
• Experience with document analysis pipelines and preprocessing for retrieval.
• Experience with automated evaluation and testing for LLM features.
• Experience with LLM tracing and analytics.

Benefits and Perks

• The opportunity to work on cutting-edge AI technology with real-world applications.
• Collaborative and innovative work environment.
• Professional development and growth opportunities.
• Flexible remote work arrangements.
• Access to the latest tools and technologies in AI and backend development.
• Competitive compensation package.
• Health and wellness benefits.
• Generous PTO policy.
• Remote stipend for home office setup and utilities.
• Opportunity to be part of a company shaping the future of industrial AI.

• Ensure your portfolio includes examples of AI feature development and deployment.
• Highlight any experience with RAG systems, retrieval concepts, and LLM applications.
• Practice explaining complex technical concepts in simple terms for non-technical stakeholders.
• Prepare to discuss your approach to debugging production systems and your experience with observability tooling.
• Be ready to talk about your understanding of cloud and DevOps concepts and how they apply to your work.
• Emphasize your ability to work independently and as part of a team, with a strong focus on shipping production-ready solutions.
• Consider learning about MAIA's specific technology stack and being prepared to discuss how your skills align with their requirements.