Senior AI Engineer, Python - (Logistics, Data & ML)

We are on the lookout for a Senior AI Engineer to join the Logistics Data & Machine Learning Platform team  on our journey to always deliver amazing experiences. 

In our Logistics Team, you’ll tackle high-impact challenges that make last-mile delivery efficient, affordable, and sustainable. Your work will directly improve experiences for riders, end customers, and merchants across the globe. Each enhancement you contribute will help Delivery Hero optimize delivery operations, supporting expansion into new areas like grocery and retail.

The Data & ML Platform team’s mission is to enable teams across the organization to build, deploy, and scale intelligent products powered by data and machine learning. As part of this team, you will work on the core AI agent platform, building reliable infrastructure and services that power production AI applications used across the company.

As a Senior AI Engineer, you will help design and operate scalable systems that enable LLM-powered agents, intelligent automation, and data-aware AI services. This role combines AI engineering, platform engineering, and ML systems design, with a strong focus on reliability, scalability, and production impact.

In this role you will,

• Build and scale AI agents capable of reasoning, tool calling, and interacting with internal services and data systems.

• Develop and maintain Retrieval-Augmented Generation (RAG) pipelines using vector databases & semantic retrieval systems to enable agents to access knowledge and enterprise data.

• Design and implement evaluation frameworks & safety guardrails for agent behaviour, model performance, and response quality.

• Implement observability, monitoring, and cost visibility for LLM and agent workloads to ensure reliability, performance, and operational efficiency.

• Collaborate closely with data scientists, ML engineers, and platform engineers to integrate AI systems with the broader data and ML ecosystem.

• Optimize systems for performance, reliability, and cost efficiency while operating AI workloads at scale.