Llm Application Engineer

**About A1** There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over ~90%\* reduced time. **About the Role** As an LLM Application Engineer, you will build the intelligence layer that powers A1's AI experiences. You will work at the intersection of LLMs, software engineering, and product - designing agent workflows, improving model behaviour, and turning AI capabilities into reliable user experiences. You will own problems end-to-end, from understanding user needs, designing Agentic workflows, integrating models and tools, building evaluation system and continuously improving AI behaviour in production. **Focus** * Build and ship LLM-powered applications and AI agent workflows * Design systems for reasoning, planning, memory, tool uuse and multi-step execution * Build reliable orchestration pipelines that turn probabilistic model outputs into predictable, observable, and safe actions * Integrate LLMs with APIs, databases, search, internal services, and external tools. * Develop prompting, context engineering, structured outputs, tool-calling, and other techniques to improve model behaviour * Build evaluation frameworks and datasets to measure AI quality, reliability, and regressions * Debug AI systems across the entire stack—from model behaviour and prompts to orchestration, backend services, and product UX * Optimise AI systems for quality, latency, and cost * Work closely with product and engineering teams to turn ambiguous product problems into working AI solutions * Establish production practices for observability, tracing, experimentation, evaluation, and continuous improvement **Tech Stack** * Python * LLM APIs and model providers, including OpenAI-compatible APIs and open-weight models * Agent frameworks and orchestration systems * Vector databases and retrieval systems * Backend services, APIs, and distributed systems * PyTorch / JAX **Ideal Experience** * Strong software engineering fundamentals with experience building AI-powered applications * Hands-on experience with LLMs, generative AI, or agent-based systems * Experience designing prompts, workflows, evaluations, or AI behaviour * Ability to write clean, production-quality code * Comfortable working across abstraction layers (model → system → product) * Strong problem-solving skills in ambiguous, fast-moving environments * Bias toward shipping, iteration, and continuous improvement **Outcomes** * AI features reach production quickly and deliver measurable user impact * LLM-powered workflows are reliable, scalable, observable, and maintainable * AI quality improves through systematic evaluation, experimentation, and iteration * AI workflows become increasingly predictable, efficient, and cost-effective * Complex AI capabilities are translated into simple, intuitive user experiences Find more [English Speaking Jobs in Germany](https://www.arbeitnow.com/english-speaking-jobs) on Arbeitnow