Senior Ai Engineer

Overview

As a Senior AI Engineer at Typeform you design, build, and operate the AI systems behind our products. You work on generative AI capabilities, enterprise RAG, and agentic workflows, turning prototypes into production services. You’ll define technical standards for AI development, evaluation, deployment, and monitoring across Typeform. You join a collaborative AI Engineering team to deliver scalable, secure customer experiences powered by AI.

Responsibilities

• Design, build, and deploy generative AI capabilities across Typeform’s products
• Develop applications using large language models, RAG, vector search, and agentic systems
• Create APIs and services for product teams to integrate AI into customer experiences
• Turn prototypes into reliable production systems with measurable performance and quality
• Explore new ways for customers to collect, understand, and act on information using AI
• Design and operate machine learning services and workflows using Python, Docker, Kubernetes, and AWS
• Build scalable data pipelines for batch and real-time processing (e.g., Kafka, Airflow)
• Use vector databases for retrieval, recommendations, personalization, and semantic search
• Manage experiments, versions, registries, and deployments with MLflow
• Improve reliability, performance, scalability, and cost efficiency of AI systems
• Establish reusable patterns and standards for building, evaluating, and releasing AI systems
• Collaborate with Product, Engineering, Data Science, Data Engineering, and Analytics teams to align AI efforts with business needs
• Communicate technical concepts, risks, and tradeoffs to technical and non-technical partners
• Contribute to technical planning and shape AI direction across Typeform

Key requirements

• Four+ years of experience building and deploying machine learning or AI systems in production
• Strong Python and software engineering skills
• Experience building production services with FastAPI or similar frameworks
• Practical experience with generative AI using LLMs, RAG, tool use, or agentic systems
• Experience with PyTorch, LangChain, LangGraph or similar
• Understanding of enterprise RAG systems (chunking, embeddings, retrieval, reranking, evaluation, monitoring)
• Experience creating automated evaluations for generative AI applications
• Experience with AWS, Docker, Kubernetes, Terraform, and CI/CD
• Experience using AWS SageMaker or AWS Bedrock
• Experience with Kafka, vector databases, or real-time/high-dimensional data processing
• Experience managing ML workflows with MLflow
• Experience monitoring production systems with Datadog or OpenSearch
• Ability to balance quality, speed, reliability, scalability, and cost in technical decisions
• Strong communication and cross-functional collaboration skills

• Strong communication skills
• Collaborative mindset
• Analytical thinking
• Python
• FastAPI
• AWS

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