Machine Learning Engineer – Retail & Buying; m​/f​/d

Join the limango IT!

As a Machine Learning Engineer in our Retail ML team, you will take end‑to‑end ownership of designing, developing and maintaining advanced machine learning solutions that directly influence our commercial performance. Your work will span dynamic pricing, demand forecasting and data‑driven buying decisions, where you will build, optimize and product ionize models operating at scale.

You will explore and implement LLM‑based methods and agentic AI approaches to enhance automation, decision support and model interpretability in real retail scenarios. A key part of your role will be building reliable, scalable data pipelines and ensuring high‑quality data flows across our ML ecosystem.

You will collaborate closely with business stakeholders, product teams and data specialists to translate complex retail challenges into measurable ML outcomes, ensuring that models deliver tangible business value. You will join a focused team of three Machine Learning Engineers and report directly to the ML Retail Team Lead.

Technical expertise

• Proven experience as a Machine Learning Engineer (mid‑senior), working with end‑to‑end ML systems in production.
• Strong command of Python (data processing, model development, performance optimization) and SQL (analytical queries, data modelling).
• Practical experience with ML frameworks such as Tensor Flow, scikit‑learn, XGBoost and/or Light
  
  GBM, including model training, tuning and deployment.
• Hands‑on experience with Google Cloud Platform (GCP) services used in ML workloads (e.g., Cloud Storage, Big Query, Vertex AI, Cloud Functions).
• Familiarity with Apache Airflow for orchestrating data pipelines and ML workflows.
• Fluency in English for technical documentation and cross‑team collaboration.

Nice to have

• Domain knowledge in retail, e‑commerce or supply chain, especially data patterns and operational constraints.
• Experience building and maintaining pricing, demand forecasting or other time‑series models in production environments.
• Understanding of ETL/ELT pipelines, MLOps practices, CI/CD for ML, feature stores and model monitoring.
• Hands‑on experience with Large Language Models, prompt engineering, fine‑tuning or integrating LLMs into production systems.

• Strong analytical reasoning and a structured approach to designing ML solutions.
• Curiosity and willingness to experiment with emerging technologies, architectures and modelling techniques.
• Ability to explain complex ML concepts, trade‑offs and model behaviour to non‑technical stakeholders.
• Proactive, ownership‑driven mindset with focus on delivering reliable production systems.
• Comfort working with incomplete information, evolving requirements and iterative discovery.
• Team‑oriented attitude, openness to code reviews, architectural discussions and collaborative problem‑solving.

• Hybrid working model for people from Munich,
  remote working for people from other locations.
• A Culture That Feels Like Home, open, team‑oriented atmosphere.
• Room to Grow, take ownership of your work with creative freedom in a fast‑moving company.
• Work in the Heart of Munich, our office is centrally located.
• Continuous Learning, access to language courses and tailored development programs.
• Perks That Matter, we subsidize your MVG ticket, EGYM Wellpass or Job Bike to support your mobility and well‑being.
• Company Events, regular company events that our culture and bring the team together.