Senior Ai Engineer

Typeform·Germany (Remote) ; Ireland (Remote); Netherlands (Remote) ; Portugal (Remote) ; Spain (Remote) ; United Kingdom (Remote)Remotesenior

In short

→Diseñar y operar sistemas de IA de producción usando LLMs, RAG y flujos agente.
→Trabajar en el ciclo completo: prototipos → sistemas confiables, escalables y monitorizados.
→Destacado: liderar estándares técnicos para IA en una empresa con 500M de respuestas al año.

Proficiency in English required for cross-team collaboration.

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What they ask for

✓Experiencia en ingeniería de IA con LLMs y RAG.
✓Construcción de pipelines de ML escalables en Python, Docker y Kubernetes.
✓Gestión de modelos con MLflow y orquestación con Airflow/Kafka.
✓Experiencia en vector databases y búsqueda semántica.
✓Capacidad para convertir prototipos en sistemas de producción.
✓Conocimiento en evaluación y monitoreo de calidad de IA (latencia, fiabilidad, coste).

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

PythonDockerKubernetesAWSKafkaAirflowMLflowVector databasesLLMsRAG

Who should you write to at Typeform?

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.

Who we are Typeform is a refreshingly different form builder. We help over 150,000 businesses collect the data they need with forms, surveys, and quizzes that people enjoy. Designed to look striking and feel effortless to fill out, Typeform drives 500 million responses every year—and integrates with essential tools like Slack, Zapier, and Hubspot. Typeform is fully remote by design. For this role, we can hire candidates based in the UK, Ireland, Germany, Portugal, Spain or The Netherlands About the team The AI Engineering team builds the systems and capabilities behind Typeform’s products. We use machine learning, large language models, RAG, and agentic systems to help customers collect, understand, and act on information in more conversational and personalised ways. The team owns the journey from experimentation through to production. This includes AI application development, evaluation, infrastructure, deployment, observability, reliability, and performance. You will work closely with Product Managers, Software Engineers, Data Scientists, Data Engineers, and Analytics teams to turn promising AI ideas into secure, scalable, and dependable customer experiences. About the role As a Senior AI Engineer at Typeform, you will design, build, and operate the systems behind our AI products. Your work will span generative AI applications, enterprise RAG systems, agentic workflows, model evaluation, machine learning pipelines, and the infrastructure required to run them reliably at scale. This is a hands on engineering role with strong ownership. You will help turn ideas and prototypes into production systems used by our customers. You will also help define the technical standards for developing, evaluating, deploying, and monitoring AI across Typeform. Things you will do Build and deliver AI products • Design, build, and deploy generative AI capabilities across Typeform’s products. • Develop applications using large language models, RAG, vector search, and agentic systems. • Build services and APIs that allow product teams to integrate AI capabilities into customer experiences. • Turn prototypes into reliable production systems with clear measures of performance and quality. • Explore new ways for customers to collect, understand, and act on information using AI. Build scalable AI systems • Design and operate machine learning services and workflows using Python, Docker, Kubernetes, and AWS. • Build reliable pipelines for batch and real time processing using technologies such as Kafka and Airflow. • Design solutions using vector databases to support retrieval, recommendations, personalisation, and semantic search. • Use MLflow to manage experiments, model versions, registries, and deployments. • Improve the reliability, performance, scalability, and cost efficiency of our AI systems. Evaluate and improve AI quality • Build automated evaluation pipelines for generative AI applications. • Develop benchmarks that measure accuracy, relevance, reliability, fairness, latency, and cost. • Evaluate retrieval strategies, including chunking, embeddings, context selection, and reranking. • Monitor AI systems in production and identify opportunities to improve their quality and performance. • Create safeguards that reduce unexpected behaviour and protect customer data. Shape our AI engineering practice • Establish re

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