Design and implementation of end-to-end GenAI and ML infrastructures that cover the complete lifecycle of AI use cases – from data preparation to training and fine-tuning of models (e.g., LLMs) to trigger systems for continuous re-training, provision of models as services, and monitoring.
Planning, setup and administration of operating platforms (on-premise or cloud) for GenAI and ML applications, based on modern technologies such as Kubernetes or modern container orchestration approaches – as well as analysis and optimization of existing platforms at the customer's site.
Provisioning and deployment of local LLMs and Generative AI models on the platform, including integration of tools and APIs for GenAI workflows and applications.
Supporting data scientists, data engineers, and machine learning engineers in creating interfaces, data preparation, and workflows.
Partnership-based consulting for our clients in all project phases: from requirements definition and concept development to implementation and successful go-live.
Responsibility for quality and project success as well as active participation in the further development of our consulting portfolio, especially in the area of GenAI and LLM infrastructures
Consulting means flexibility: Your project assignment depends on our clients and your project situation – you work throughout Germany in our offices, on-site at the client's premises and from home.
Qualifications
Completed studies in (business) informatics, (business) mathematics, (business) engineering or a comparable field of study, ideally with a focus on machine learning, artificial intelligence or cloud technologies.
Excellent knowledge and several years of professional experience in container-based architectures (e.g., Docker) and platforms such as Kubernetes or Openshift, especially in the context of infrastructure for GenAI and ML.
Solid knowledge and experience with relevant tools and frameworks for the ML and GenAI lifecycle, such as Hugging Face, NVIDIA AI Enterprise, vLLM and LangChain, as well as techniques for training, fine-tuning (e.g. LoRA, prompt tuning) and deployment of models.
Knowledge and experience with cloud solutions such as AWS, Azure, Google Cloud or IBM Cloud, especially in the area of deploying ML and GenAI applications.
Extensive experience with automation and development tools, including Python, scripting tools (e.g., Ansible, Terraform), software development tools (e.g., Git, Gitlab, Jenkins), and Linux-based operating systems.
Target audience-oriented communication of complex, technical issues and experience in large IT landscapes and organizations, ideally in the public sector.
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