
Aufgaben Do you want to contribute to our dynamic and growing services company with your Machine Learning, AI, and Software Engineering knowledge? Do you want to act as a thought leader and trusted advisor in the field of Data Products and Data Mesh ? We are looking for a German-speaking Senior Data Engineering Consultant who will be involved in the whole lifecycle of projects, both internally and externally: Consulting, Engineering & Training : You perceive data, software, and AI engineering as key capabilities for mastering the challenges of our clients' digital transformations, want to help them understand both their potential and their limitations, and deliver impactful, valuable services. Requirement Analysis : You analyze customer requirements and identify and define best-fit solutions. Implementation of Data Pipelines and Platforms, ML/LLM Integrations, Reliability Engineering & Operationalization : You understand how to successfully deliver data projects from the prototype or pilot phase into production, design, build, integrate and test data pipelines and platforms, and implement engineering best practices such as traceability, reliability, scalability, measurability, and automation within a demanding project and technology environment. Concept Development : You contribute to our solution blueprints and concepts (e.g., our journey for ‘** Reliable Data Products & Efficient Data Meshes’ **). Expertise & Thought Leadership : You strive to become an expert and a trusted advisor in the field of Data Platforms, Data Products, and DataOps Ownership, Communication, Knowledge Sharing & Teamwork: You take ownership of your work, present your results to various stakeholders, share your knowledge, and collaborate (pro-)actively with our and your client’s teams.

At Machine Learning Architects Basel (MLAB), we assist and empower people and organizations in designing, building, and operating reliable data and machine learning solutions. In doing so, our data and AI journeys and effective solution patterns enable our customers to operationalize, scale, and continuously deliver data and AI products beyond the pilot and prototype stages . These patterns and frameworks revolve not only around the latest technologies but also consider role, skills, and process adjustments.