Senior AI Engineer

New

TypeformAI software

For this role, we can hire candidates based in the UK, Ireland, Germany, Portugal, Spain or The NetherlandsFull-TimeSenior

Salary not disclosed

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Job Details

Experience: At least four years of experience building and deploying machine learning or AI systems in production.
Required Skills: AWSPythonKubernetesMLFlowFastAPILLMGenerative AILangChain

Have at least four years of experience building and deploying machine learning or AI systems in production.
Bring strong Python and software engineering skills.
Have experience building production services with Python frameworks such as FastAPI.
Have practical experience developing generative AI applications using large language models, RAG, tool use, or agentic systems.
Have experience with PyTorch, LangChain, LangGraph, or similar frameworks.
Understand enterprise RAG systems, including chunking, embeddings, retrieval, reranking, evaluation, and monitoring.
Have experience creating automated evaluations for generative AI applications.
Have experience with AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices.
Have experience using AWS SageMaker or AWS Bedrock.
Have experience with Kafka, vector databases, or technologies for real-time and high-dimensional data processing.
Have experience managing machine learning workflows using MLflow.
Have experience monitoring production systems with tools such as Datadog or OpenSearch.

Design, build, and deploy generative AI capabilities across Typeform products, including Research Flow.
Develop applications using large language models, RAG, vector search, and agentic systems.
Build services and APIs that let product teams integrate AI capabilities into customer experiences.
Design and operate machine learning services and workflows using Python, Docker, Kubernetes, and AWS.
Build reliable batch and real-time processing pipelines.
Create automated evaluation pipelines and benchmarks for generative AI applications.
Evaluate retrieval strategies, monitor production AI systems, and identify quality and performance improvements.
Establish reusable patterns and technical standards for building, evaluating, and releasing AI systems.
Partner with Product, Engineering, Data Science, Data Engineering, and Analytics teams to deliver production AI systems.

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