Principal ML Engineer (Agentic AI)-Vendor Data Team

We are on a lookout for a hands-on Principal ML Engineer (Agentic AI) to join the Vendor Data Team on our journey to always deliver amazing experiences to go beyond prompt engineering into autonomous orchestration: designing agents that generate their own prompts, tools that empower AI with real-world actions, and judge models that validate outputs. Your work won’t sit in research notebooks — it’ll ship.

As part of our Vendor Team, you’ll be the driving force behind the success of thousands of restaurants, shops, and local businesses. Your contributions will empower vendors with advanced tools to manage their operations, boosting their visibility and reach. Every feature you help build will create growth opportunities for businesses of all sizes, strengthening Delivery Hero’s ecosystem and impact.

In this role, you will go beyond prompt engineering into autonomous orchestration: designing agents that generate their own prompts, tools that empower AI with real-world actions, and judge models that validate outputs. The Agentic AI team is building the next generation of AI-native products—intelligent systems that reason, act, and adapt. We combine the power of LLMs, autonomous agents, and RAG to move beyond static prompts into dynamic AI systems that solve real problems at scale.

Key Responsibilities:

• Design and own end-to-end ML and data systems — from ingestion and transformation to model integration and production deployment

• Architect and maintain scalable data pipelines for RAG, embeddings, and real-time/near-real-time data processing

• Build and operate production-grade ML services and APIs, ensuring reliability, scalability, and performance

• Define standards for infrastructure, deployment, and system reliability, including Infrastructure as Code, containerization, and orchestration

• Integrate ML systems with external APIs, tools, and operational platforms, enabling real-world actions and automation

• Establish monitoring, evaluation, and observability frameworks across data, models, and systems, while mentoring engineers and setting technical direction.