Senior AI Engineer
New
T
TypeformAI software
United States (Remote). For this role, we can hire candidates based in the ET timezone in the US., ET timezoneFull-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
- AWSPythonKubernetesMLFlowPyTorchFastAPILLMGenerative AILangChain
Requirements
- At least 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 Python frameworks such as FastAPI.
- Practical experience developing generative AI applications using large language models, RAG, tool use, or agentic systems.
- Experience with PyTorch, LangChain, LangGraph, or similar frameworks.
- Strong understanding of enterprise RAG, including chunking, embeddings, retrieval, reranking, evaluation, and monitoring.
- Experience creating automated evaluations for generative AI applications.
- Experience with AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices.
- Experience using AWS SageMaker or AWS Bedrock.
- Experience with Kafka, vector databases, or other technologies for real-time and high-dimensional data processing.
- Experience managing machine learning workflows using MLflow.
- Experience monitoring production systems with tools such as Datadog or OpenSearch.
Responsibilities
- 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.
- Turn prototypes into reliable production systems and define measures of performance and quality.
- Design and operate machine learning services and workflows using Python, Docker, Kubernetes, and AWS.
- Build batch and real-time processing pipelines using technologies such as Kafka and Airflow.
- Build automated evaluation pipelines and benchmarks for generative AI applications.
- Monitor production AI systems and improve their quality, performance, reliability, scalability, and cost efficiency.
- Establish reusable technical standards for building, evaluating, deploying, and monitoring AI systems.
- Partner with Product, Engineering, Data Science, Data Engineering, and Analytics teams to develop production AI services.
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