AI Platform Engineer

As an AI/ML Platform Engineer , you will design, build, and maintain scalable infrastructure to support advanced AI and machine learning operations. You will play a central role in integrating large language models and skill graph-related capabilities into distributed, cloud-based systems, ensuring reliability, scalability, and efficiency. Beyond infrastructure, you will contribute to data orchestration and model development, helping to strengthen the company’s next-generation AI solutions.

Responsibilities

• Break down product requirements into actionable engineering tasks for the team.
• Communicate data constraints clearly to engineering, product, and stakeholder groups.
• Integrate AI/ML models into distributed cloud architectures (AWS preferred).
• Design and implement scalable infrastructure using cloud services (e.g., Fargate, Lambda, ECS).
• Develop and maintain robust data pipelines and orchestration processes.
• Enhance automated deployment, logging, and monitoring frameworks.
• Contribute to text analysis and NLP models to extract and structure key business data.

Qualifications

• University degree in Computer Science, Data Science, Statistics, or a related field.
• 4+ years of experience building data-centric software frameworks, including infrastructure.
• Proficiency in Linux-based development environments.
• Experience with container technologies (Docker), version control (GitLab/GitHub), and CI/CD pipelines.
• Agile mindset with exposure to 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 experience with cloud‑native development (preferably AWS).
• Proven track record in large‑scale data pipeline orchestration.
• Experience preparing and manipulating structured, semi‑structured, and unstructured datasets for model evaluation.

Preferred Skills

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

Location: Munich, Bavaria, Germany

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