Bike Mobility Services GmbHlease-a-bike.de
Senior Machine Learning & AI Engineer (m/f/d)
- Standort
- Cloppenburg
- Remote
- Hybrid
- Anstellungsart
- Vollzeit
- Erfahrung
- Mid-Level
- Bereich
- Software, Daten & IT
Anforderungen auf einen Blick
- 5+ years Data Science (Erforderlich)
- Python (Erforderlich)
- SQL (Erforderlich)
- ML frameworks (Erforderlich)
- AWS (Erforderlich)
- Snowflake (Erforderlich)
- Generative AI and Agents (Erforderlich)
- English (Erforderlich)
- Process redesign (Wünschenswert)
- Leasing domain experience (Wünschenswert)
- German or Dutch (Wünschenswert)
ErforderlichWünschenswert
vor 1 Tag
Do you want to redesign how a company runs with AI, from how we find customers to how we manage residual values and answer service requests? Do you enjoy moving between classic ML and LLM agents, and want to shape an AI team in an environment where your decisions stick?
We are looking for a Senior Machine Learning & AI Engineer (m/f/d) to build ML and agent solutions on AWS and Snowflake and to set the technical foundation of a growing AI team.
Who are we?
Bike Mobility Services GmbH, a subsidiary of the Dutch company Pon.Bike, is part of a group that owns international brands like Kalkhoff, Cervélo, Urban Arrow, Cannondale, Santa Cruz, Focus, and Royal Dutch Gazelle. Our mission is to get as many employees as possible onto bikes for their commute, through our digital Lease a Bike portal. Data already drives how we steer our portfolio, manage residual values, and find new customers.
You will take a business process, understand where the potential lies, and rebuild it with AI: predictive models where decisions need numbers, agents where people spend their day on repetitive work.
Core Responsibilities:
- Predictive ML: Build models for lead generation, demand forecasting, residual value forecasting, portfolio steering, and fraud detection.
- Agents & Generative AI: Redesign workflows in customer service and other teams with RAG and agent-based solutions, integrated securely into our systems and evaluated for output quality.
- MLOps / LLMOps: Build and run ML pipelines on AWS and Snowflake. Monitor performance, drift, and cost, and decide when a model needs retraining or should be retired.
- Business Partnership: Turn business needs into measurable use cases together with stakeholders and the data engineering team, and own delivery end to end.
- Team Building: Evaluate new models and frameworks, shape shared standards and tooling, and mentor more junior colleagues as the team grows.
Across all of this, you keep responsible AI in view: data privacy, bias, and explainability.Core Responsibilities:
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