Senior Fullstack Applied AI Engineer

We are looking for a Fullstack Applied AI Engineer (f/m/x) to join our engineering team - someone who ships production-grade AI features end-to-end, from retrieval pipelines and agentic workflows to the APIs and UI changes that bring them to life. Tasks

• You build and evolve MAIA's core product capabilities. Your primary focus is backend and AI systems - RAG pipelines, retrieval strategies, agentic workflows, and LLM integrations. You also implement the frontend changes needed to ship features end-to-end, while backend remains your main area of work.
• You design, implement, and ship features from discovery to production rollout, with a strong focus on backend and AI functionality.
• You build and iterate on RAG pipelines, retrieval strategies, and LLM integrations, making pragmatic tradeoffs across quality, latency, and cost.
• You build evaluation approaches for LLM features - quality gates, regression detection, and observability that makes non-deterministic systems debuggable.
• You implement frontend changes needed to ship features end-to-end - simple, intuitive UI work that unblocks delivery, typically a small portion of total effort.
• You proactively scout new technologies and approaches, translate findings into production-ready solutions, and push the team's understanding of what is possible.
• You collaborate closely with Product, DevOps, and customer-facing teams to turn requirements into high-quality, maintainable software.

• Strong backend engineering skills in TypeScript, or another strongly typed language (Go, Java, Rust) plus solid TypeScript fundamentals and the ability to contribute productively in a TypeScript codebase from day one.
• Proven experience building and shipping production REST APIs.

Strong SQL skills, ideally with PostgreSQL in production. * Hands-on experience building or operating LLM applications, ideally 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 (logs, metrics, tracing), for example Grafana, Sentry, PostHog, or equivalents.

• Solid React fundamentals and the ability to implement and maintain frontend components with good engineering quality and basic UX judgment.
• Foundational understanding of cloud and DevOps concepts, enough to collaborate effectively with a dedicated DevOps engineer and handle small operational tasks if needed.
• Ability to follow secure development best practices in day-to-day engineering work.
• Fluent in English. German is a plus.

Ways of working

• Strong ownership mentality - you take responsibility end-to-end, without needing to be managed.
• People enjoy working with you - not just because of what you know, but because of how you interact with them.
• AI is part of how you work - not just as a tool you use, but something you actively explore. You experiment with models, prompts, agents, and workflows, and you have informed opinions about what actually works.
• Pragmatic builder - you ship solutions at the right level of complexity for the stage, and you can explain why a simpler approach was the right call.
• You prioritize based on business impact and customer value, not technical interest alone.
• Comfortable working in a fast-moving startup environment with ambiguity and shifting priorities.
• You communicate clearly with both technical and non-technical stakeholders.

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 (e.g., Langfuse).
• Technical leadership: mentoring, setting standards, or leading cross-cutting initiatives.

What we offer you:

• A central role in the engineering team of a well-funded, fast-growing AI startup building for the industrial sector - where the AI problems are genuinely hard and the impact is tangible.
• Short decision-making paths, real ownership, and a team that trusts you to drive your work.
• Direct impact on product quality, AI capability, and company growth.
• Flexible working hours and fully remote in Germany
• Access to a WellPass fitness membership for your physical and mental wellbeing.
• Competitive salary of 75,000 - 85,000 EUR and VSOP (Virtual Stock Option Plan) participation opportunities.

MAIA * Leipzig, Sachsen * € 75.000-85.000 pro Jahr * Unbefristet * Vollzeit * 15 Stunden her MAIA is the AI platform built for industrial companies - where generic AI tools break down because the data is complex, the stakes are high, and precision actually matters. We focus on the hard problems: integrating with real engineering workflows, understanding complex technical documents, and turning implicit organizational knowledge into something the whole company can build on. Our goal is to become the leading AI knowledge management platform for industrial companies in Europe - built for the companies that shape the physical world. Our team consists of structured organizational talents, creative minds, and technology-enthusiastic engineers, all with several years of experience in B2B SaaS and AI., We are a remote-first company and have been since day one. Most of our team is based in Leipzig, but remote team members are a natural part of how we work - not an exception. We do bring the full team together in Leipzig, or somewhere else, a few times a year for team events and planning sessions, with all travel and accommodation costs covered. If you are somewhere in Germany you are in scope. Hiring timeline: We are aiming to have this role filled by May 2026. The process consists of three stages: an intro call (30min), a technical interview (90min) and a final conversation with leadership. We move quickly and will keep you informed at every step. If this role sounds like the right next step for you, we would love to hear from you. MAIA We are driven by our passion for innovation! MAIA is the AI platform built for industrial companies - the places where generic AI tools break down because the data is complex, the stakes are high, and precision actually matters. We focus on the hard problems: integrating with real engineering workflows, understanding complex technical documents, and turning implicit organizational knowledge into something the whole company can build on. Our team brings together sharp organizational thinkers, creative problem solvers, and engineers who care deeply about what they build.