AI Platform Engineer

Das ist der Job

Strong proficiency in Python or Go, with a track record of building scalable, production-grade applications.

Darum lohnt es sich

More than just managing infrastructure, you will be actively building the cohesive, end-to-end platform that enables our teams to develop, deploy, and scale applications rapidly.

Collaborate and Enable: Work closely with software and data science teams to understand their needs, gather requirements for the platform, and provide the tools and support that accelerate their work.

Familiarity with modern JavaScript/TypeScript and a UI framework (e.g., React, Vue) for building internal tools is a significant plus. #J-18808-Ljbffr Responsibilities As an AI Platform Engineer, you will be a key architect of the software and systems that empower our AI-driven initiatives.

You will engineer robust backend services, create internal tools, and design the automated workflows that form the foundation of our AI capabilities. Develop Core Platform Services: Design, build, and operate the backend services and APIs that serve our AI models and manage the ML lifecycle.

Develop the Data Ingestion Platform: Design, build, and maintain the services and automated workflows responsible for populating our vector and graph databases, ensuring our AI applications have access to timely and accurate data.

Build Full-Stack Tooling: Create user-facing tools and internal dashboards that allow developers, data scientists and managers to interact with the AI platform. Own the Application & Model Lifecycle: Implement and maintain the core platform components and ensure the performance of production systems.

Requirements Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience. Proven experience as a Software Engineer, Platform Engineer, or an AI/MLOps Engineer with a strong software development background.

Experience designing and building APIs and backend services, ideally with frameworks like FastAPI. A solid understanding of AI/ML concepts, particularly Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and vector databases. Hands‑on experience with Docker and Kubernetes for containerization and orchestration.

Experience with Infrastructure as Code (e.g., Terraform) and cloud platforms (preferably AWS).