ai engineer for conversational experiences

Enfint; Deutschland; Hybrid; EUR 90.000 - 120.000; Vollzeit

Zusammenfassung

Typeform ищет опытного инженера по искусственному интеллекту для разработки и внедрения генеративных возможностей в продуктах компании. Вы будете работать над созданием сервисов и API, которые интегрируют ИИ в взаимодействие с пользователями.

Требуется опыт работы с Python, AWS, Docker и Kubernetes, умение проектировать пайплайны данных, оценивать качество моделей и обеспечивать масштабируемость.

Qualifikationen

• Опыт разработки генеративных ИИ-приложений с использованием больших языковых моделей, RAG и встроений.
• Опыт развёртывания production-сервисов на AWS, Docker и Kubernetes.
• Практический опыт работы с MLflow, Datadog и OpenSearch.
• Знание Python и фреймворков (FastAPI) и умение строить надежные API.
• Понимание процессов CI/CD и опыт работы с потоками данных (Kafka, Airflow).

Aufgaben

• Разрабатывать и внедрять генеративные ИИ-возможности в продуктах компании.
• Создавать сервисы и API, интегрирующие ИИ в пользовательский опыт.
• Проектировать и управлять конвейерами данных и обучением моделей.
• Обеспечивать измеряемое качество, безопасность и мониторинг систем.
• Сотрудничать с командами Product, Engineering и Data.

Kenntnisse

Python

FastAPI

Software Engineering

ML/AI Systems

Communication

AWS

Docker

Kubernetes

RAG / LLM

Vector Databases

Tools

MLflow

Datadog

OpenSearch

Airflow

Kafka

Terraform

LangChain

LangGraph

AWS SageMaker

AWS Bedrock

Jobbeschreibung

Описание:

Typeform enables conversational, human-centered experiences by helping customers collect, understand, and act on information. Its AI Engineering team builds the systems behind these products using machine learning, large language models, RAG, and agentic systems.

Задачи:

• 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 integrate AI capabilities into customer experiences;
• Turn prototypes into reliable production systems with clear performance and quality measures;
• 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 reliable batch and real-time processing pipelines using Kafka and Airflow;
• Design vector database solutions for 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 AI systems;
• Build automated evaluation pipelines for generative AI applications;
• Develop benchmarks measuring 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 quality and performance;
• Create safeguards that reduce unexpected behaviour and protect customer data;
• 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 engineering practices across testing, security, observability, version control, and deployment;
• Share technical knowledge and support the development of other engineers;
• Keep up with relevant AI research, tools, and engineering practices and apply useful developments;
• Partner with Product, Engineering, Data Science, Data Engineering, and Analytics teams;
• Work with Data Scientists to turn experiments and models into reliable production services;
• Communicate technical concepts, risks, and tradeoffs to technical and nontechnical partners;
• Contribute to technical planning and help shape the direction of AI across Typeform.

Требования:

• 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 technologies;
• Strong understanding of enterprise RAG systems, 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 with AWS SageMaker or AWS Bedrock;
• Experience with Kafka, vector databases, or technologies for real-time and high-dimensional data processing;
• Experience managing machine learning 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 skills and experience collaborating with Product, Engineering, and Data teams;
• Nice to have: experience in a B2B SaaS product company, Airflow or Argo Workflows, SQL, Spark, Snowflake or other data processing technologies, systems combining structured data, unstructured data, and generative AI, AI security, privacy, responsible AI, prompt injection protection, data leakage prevention, improving AI system latency and cost at scale.

Условия:

Work location: European Economic Area; Relocation support is not specified.