LLM Application Engineer

Bjak·Germanymid

In short

→Construyes experiencias de IA con agentes autónomos que actúan en tareas reales sin mucho input del usuario.
→Diseñas flujos de trabajo de IA, integras modelos y herramientas, y garantizas fiabilidad en producción.
→Destaca que el foco es crear inteligencia proactiva, no solo respuestas (reducción de hallucinaciones, persistencia de contexto).

Se requiere nivel intermedio para avanzado en inglés (comunicación técnica y escrita)

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The questions they'll ask you

1. ¿Cómo diseñarías un flujo de trabajo de agente para completar una cita médica con seguimiento automático?

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💵 USD · Remote · No visa

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What they ask for

✓Experiencia en desarrollo de aplicaciones con LLMs o sistemas basados en agentes.
✓Habilidad para diseñar prompts, flujos de trabajo y evaluar el comportamiento de IA.
✓Capacidad para escribir código limpio y de producción en Python.
✓Conocimiento en integración con APIs, bases de datos, herramientas externas y servicios distribuidos.
✓Experiencia con evaluación de modelos y sistemas de prueba de calidad en IA.
✓Capacidad para trabajar en entornos ambiguos y priorizar el envío rápido iterativo.

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

PythonLLM APIs y proveedores (OpenAImodelos de peso abiertoframeworks de agentes y orquesbases de datos vectorialessistemas de recuperaciónservicios backendAPIssistemas distribuidosPyTorch

Who should you write to at Bjak?

Your free dossier identifies the people who'd interview you — their background, what they value, and how to reach out so you stand out before applying.

About ActAI There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations. Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things. About the Role As an LLM Application Engineer, you will build the intelligence layer that powers ActAI'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. 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 on Arbeitnow

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