ml engineer for agentic AI

Delivery Hero operates a local delivery platform across around 65 countries, connecting restaurants, shops, and local businesses with customers. Its Vendor Data Team builds AI-native products, including an AI Account Manager for food delivery platforms that automates business workflows through data pipelines, retrieval systems, and agentic components.

• 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 or near-real-time data processing;
• Build and operate production-grade ML services and APIs with 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 to enable real-world actions and automation;
• Lead the implementation of security best practices for AI systems, including secure prompt handling, data privacy protocols, and protection against adversarial attacks or model injection;
• Ensure all AI agents operate within strict authorization boundaries.

• Strong experience designing and scaling production-grade ML systems and data platforms, including large-scale deployments;
• Deep expertise in data engineering and ML pipelines, including feature and data pipelines, RAG systems, and embedding workflows;
• Proven experience building and maintaining reliable data infrastructure with strong guarantees around data quality and freshness;
• Strong engineering skills in Python and SQL, with experience in Docker, Kubernetes, and cloud environments;
• Experience with Infrastructure as Code, such as Terraform or similar, and building reproducible, scalable systems;
• Hands-on experience integrating ML systems with real-world APIs and services and operating them in production with monitoring and observability;
• Nice to have: Experience with LLMs, agent architectures, or orchestration frameworks; familiarity with synthetic data generation, evaluation systems, or AI feedback loops; experience with agent or ML observability tools; experience with model serving, routing, or inference optimization; experience with open-source models or custom inference stacks; knowledge of system reliability, failure handling, and safety patterns in AI systems.

• Hybrid work includes face-to-face collaboration at the Berlin campus 2 days a week;
• 27 Days of holiday, plus an extra day in the second and third years of service;
• €1,000 Educational budget, language courses, parental support, and access to Udemy Business;
• Health checkups, meditation, and gym benefits;
• Employee Share Purchase Plan, Sabbatical Bank, public transportation ticket discount, life and accident insurance, and corporate pension plan;
• Digital meal vouchers, food vouchers, and corporate discounts;
• Relocation support to Berlin is available.