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Ref: #73382

Python/ Platform enginer

  • Practice Development & Integration

  • Technologies Artificial Intelligence Development Skills

  • Location Munich , Germany

  • Type Permanent

About the Role

 

As an AI/ML Platform Engineer at Cobrainer, you will design, build, and maintain scalable infrastructure to support our AI and machine learning operations. You will play a central role in integrating large language models and skill graphs into distributed AWS-based systems, ensuring reliability, scalability, and efficiency. Beyond infrastructure, you will contribute to data orchestration and model development, helping to future-proof Cobrainer's Skills AI solutions.

 

 

Responsibilities

 

 

  • Break down product requirements into actionable engineering tasks for your team.
  • Explain data constraints to engineering, product, and stakeholder teams.
  • Integrate AI/ML models into distributed cloud architectures on AWS.
  • Design and implement scalable infrastructure using AWS services (Fargate, Lambda, ECS, etc.).
  • Develop and maintain robust data pipelines and orchestration processes.
  • Enhance automated deployment, logging, and monitoring setups.
  • Contribute to text analysis and NLP models to extract and structure core business data.

 

 

Qualifications

 

 

  • University degree in Computer Science, Data Science, Statistics, or a related field.
  • 4+ years of industry experience in building data-centric software frameworks, including infrastructure.
  • Strong foundation in OOP software development.
  • Proficiency in Linux-based software development.
  • Experience with container technologies (Docker), version control (GitLab/GitHub), and CI/CD pipelines.
  • Agile mindset with experience in modern development practices.
  • Fluent in written and spoken English.

 

 

Required Skills

 

 

  • Advanced fluency in modern Python development, including database management and software testing.
  • Hands-on cloud-native development experience on AWS.
  • Proven track record in large-scale data pipeline orchestration.
  • Experience preparing and manipulating datasets for model evaluation (structured, semi-structured, and unstructured data).

 

 

Preferred Skills

 

 

  • Practical experience with the latest large language model (LLM) developments.
  • Familiarity with data science frameworks (NumPy, pandas, scikit-learn).
  • Experience with deep learning frameworks (PyTorch, TensorFlow).
  • Knowledge in one or more of: entity extraction/linking, document classification, knowledge graphs, recommendations/matching.
  • Experience with orchestration and ML platforms such as Prefect, Airflow, Kubeflow, SageMaker.
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