Senior AI Engineer

Your daily business

• Design and ship end-to-end AI systems, from prototype to production.

• Build LLM-powered applications (incl. RAG, tool usage, validations) with a focus on reliability and cost-efficiency.

• Develop and operate agentic AI systems (e.g., planning, tool orchestration, multi-step workflows) with clearly defined safety and integrations into customers’ systems

• Define evaluation strategies (offline + online), including metrics, benchmarks, and monitoring.

• Optimize system performance across latency, cost, and quality.

• Take technical leadership within customer project teams, coordinating architecture, implementation, and iteration cycles.

• Act as a technical point of contact for customers, explaining trade-offs and guiding solution decisions.

• Work directly with customers and internal teams to turn ambiguous, real-world problems into concrete AI solutions.

• Contribute to architectural decisions and support less-experienced engineers through technical guidance (no people management).

Your profile

• Several years (3–6) of hands-on experience building and operating AI/ML systems in real-world projects.

• At least one practical implementation of agentic AI systems, beyond simple chains or demos, ideally in a production or near-production context.

• Strong Python expertise (≈5 years) and experience with modern AI/LLM tooling; framework-agnostic mindset.

• Proven experience with LLM-based systems (e.g., RAG, embeddings, tool usage) and an understanding of trade-offs between quality, cost, latency, and complexity.

• Ability to work independently in customer-facing environments, taking ownership of technical decisions under uncertainty.

• German language skills at C1 level (mandatory) and professional communication skills in customer contexts.

• Residence in Germany and willingness to work in a hybrid model with occasional on-site engagements.

Some bonus points to score

• Deeper experience with agentic architectures (planning, memory, multi-agent coordination).

• Experience deploying and operating AI services in cloud environments (AWS, Azure, etc.).

• Contributions to open-source projects, technical writing, or applied research (optional)