Senior Ai Engineer

typeformsenior

In short

▸Diseñar y construir sistemas de IA de vanguardia para formularios conversacionales
▸Trabajar en aplicaciones de LLM, RAG, flujos agenticos y evaluación automatizada de IA en producción
▸Destacar por definir estándares técnicos y escalar sistemas confiables en AWS con Kubernetes

Fluent English required (must be able to work in English environment)

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What they ask for

✓Experiencia comprobada en ingeniería de IA con LLMs y RAG
✓Construcción de sistemas de producción escalables con Python, Docker y Kubernetes
✓Gestión de pipelines ML con Airflow, Kafka y MLflow
✓Evaluación automática de calidad de modelos generativos
✓Experiencia con bases de datos vectoriales y búsqueda semántica
✓Capacidad para definir estándares técnicos y prácticas de ingeniería de IA

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PythonDockerKubernetesAWSAirflowKafkaMLflowvector databasesLLMsRAG

Who should you write to at typeform?

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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. 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 reusable patterns and technical standards for building, evaluating, and releasing AI systems. • Help teams make informed decisions about models, frameworks, infrastructure, performance, and cost. • Apply strong engineering practices across t

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