Hybrid Senior AI – ML Architect

at Neurawork

• Job title: Senior AI – ML Architect
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Azure, Cloud, Docker, Kubernetes, Microservices, Open Source, Python, PyTorch, Tensorflow
• Location requirements: Hybrid, Germany

About the role

• Senior AI Architect designing scalable AI systems in Azure, Hybrid and On-Prem for DACH companies. Engaging in full lifecycle management and ensuring compliance in AI projects.

Responsibilities

• Design and implementation of AI architectures in Azure, hybrid and on-premises environments
• Selection, integration and evaluation of models, tools and frameworks (e.g., LLMs, RAG, embeddings, Transformers)
• Development and enhancement of MLOps pipelines including CI/CD, deployment, monitoring and lifecycle management
• Operation of container infrastructures (Docker, Kubernetes) and automation via Infrastructure-as-Code
• Integration of AI solutions into existing enterprise architectures (APIs, microservices, data platforms)
• Scaling, performance and cost optimization for production AI systems
• Participation in pre-sales, workshops and client presentations
• Ensuring data protection, security, compliance and ethical standards in AI projects
• Prototyping of new AI approaches and active contribution to our technology radar

Requirements

• Completed degree in Computer Science, Data Science, Mathematics or a comparable field
• Several years of experience in AI/ML projects and in architecture roles
• Knowledge of Azure (Azure ML, Azure Functions, Azure Kubernetes Service, etc.)
• Experience with hybrid / on-prem solutions (private cloud, on-premises infrastructure)
• Proficiency in Python and common ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face)
• Experience working with LLMs, RAG, embeddings and prompt engineering
• Confident use of Docker, Kubernetes and infrastructure automation
• Experience with data pipelines, databases and big data technologies
• Routine in MLOps, model deployment, monitoring and versioning
• Very good German language skills, both written and spoken, and residence in Germany
• Experience in distributed remote teams and self-organized working
• Nice-to-have: consulting experience, pre-sales or client workshops, contributions to open source or academic publications, expertise in Explainable AI, AI compliance, robustness or security, experience with Edge AI or hardware accelerators (GPU, TPU, FPGA), leadership experience and project responsibility, industry knowledge (e.g., mid-sized companies, manufacturing/industry, consulting)

Benefits

• Technical freedom: from architectural decisions to innovation experiments
• High-impact projects: direct influence on business processes in the DACH mid-market
• Remote-first: flexible work with occasional client appointments or team meetings
• Training & conferences: access to new technologies, budget for training and professional exchange
• Pragmatism & impact: we focus on quality rather than hype