AI Engineer (Applied AI, B2B SaaS)

We are a fast‑growing B2B SaaS company that has built an AI‑driven platform to modernise automotive aftersales. Our solution helps bodyshops, dealer groups and multi‑site operators across Europe optimise workflows, boost efficiency and make data‑backed decisions. By using digital twins and real‑time optimisation, we turn hidden operational bottlenecks into actionable insights, enabling more accurate work planning, smarter job‑to‑person matching and reliable completion forecasts. As an AI Engineer you will design and deploy machine‑learning models that turn domain‑specific operational data into predictions and decisions. You will extend scheduling and resource‑optimisation capabilities, including multi‑objective optimisation, constraint handling and stable re‑planning. Building end‑to‑end models that continuously learn from real operational data, you will own the full AI lifecycle, from data analysis and feature engineering through model development, evaluation and production monitoring. Translating business problems into formal models and measurable outcomes is a core part of the role. The position requires a strong academic background and proven technical expertise. You need a Master's or PhD in Computer Science, Mathematics, Physics or a related discipline, together with several years of experience in machine‑learning, especially with tabular data and time‑series. You must have deep knowledge of at least one deep‑learning framework (PyTorch is preferred) and a track record of designing optimisation algorithms for complex, constrained problems. Proficiency in Python and the data‑science stack (pandas, scikit‑learn) is essential, as is experience with ML experiment tracking and model deployment tools such as MLflow, Docker and Kubernetes. Fluency in English and a self‑driven, solution‑oriented mindset are also required. Beyond the core responsibilities, experience with large language models or agentic systems (e.g., retrieval‑augmented generation, information extraction from unstructured data, API‑based workflows) is a plus. The role offers creative freedom within a young, technically ambitious team, direct impact on a real‑world product, a modern tech stack and a culture that encourages experimentation. You will enjoy end‑to‑end ownership of AI features, from data exploration to production deployment, in a domain‑rich environment that goes beyond generic SaaS. What the role asks for: - Master's or PhD in Computer Science, Mathematics, Physics or related field - Several years of machine‑learning experience (tabular data & time series) - Strong knowledge of a deep‑learning framework (PyTorch preferred) - Experience designing optimisation algorithms for constrained problems - Proficient in Python and data‑science libraries (pandas, scikit‑learn) - Experience with ML experiment tracking and model deployment (MLflow, Docker, Kubernetes) - Fluent English language proficiency - Self‑driven, solution‑oriented mindset - Experience with LLMs or agentic systems (nice to have)