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ZEZellerfeld

.AI Engineer(m/w/x)

Hamburg
Full-timeOn-siteExperienced
AI/ML

Developing AI/ML solutions for footwear manufacturing, deploying models, and building ETL pipelines. Proven ML/AI Engineer experience with shipped models and Python fluency required. Flat hierarchy, voice heard, team bonding.

Requirements

  • Proven experience as ML/AI Engineer with shipped models
  • Strong Python and fluency with data/ML stack
  • Solid ETL experience with messy data at scale
  • Hands-on deep learning experience with PyTorch/TensorFlow
  • Solid grounding in ML fundamentals
  • Software engineering discipline: clean code, version control, testing
  • Cloud experience (AWS or GCP)
  • Computer Vision experience for scanning, quality control
  • NLP for internal tools or customer-facing experiences
  • MLOps in practice (Docker, Kubernetes, CI/CD, monitoring)
  • Experience with sensor, telemetry, or manufacturing data
  • Open-source contributions in ML space

Tasks

  • Design and develop AI and machine learning solutions
  • Deploy AI and ML models in footwear manufacturing
  • Build and maintain ETL pipelines for raw data
  • Transform scan, telemetry, and production data for model training
  • Design, build, and maintain ML models and pipelines
  • Solve real production and product problems with ML
  • Own the full ML lifecycle: data acquisition, feature engineering, training, deployment, monitoring, and iteration
  • Integrate models into software and production systems
  • Run experiments and evaluate model performance
  • Collaborate with software, hardware, and product teams
  • Translate ambiguous problems into ML solutions
  • Recognize when ML is not the right tool
  • Stay current with AI and ML advancements

Work Experience

  • approx. 1 - 4 years

Education

  • Bachelor's degreeOR
  • Master's degree

Languages

  • EnglishBusiness Fluent

Tools & Technologies

  • Python
  • NumPy
  • Pandas
  • scikit-learn
  • PyTorch
  • TensorFlow
  • AWS
  • GCP
  • Docker
  • Kubernetes

Benefits

Informal Culture

  • Flat hierarchy

Purpose-Driven Work

  • Voice heard

Team Events

  • Team bonding
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