Data & MLOps Engineer (Experienced)

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 MLOps journey 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. We thereby:

• Help our customers realize the full potential of data and AI solutions, from use case identification, over data and ML platform implementation to integration and testing operation of ML models and LLMs (DataOps & MLOps).
• Design, test, integrate and operate data, model and code pipelines, and end-to-end data/ML/LLM systems.
• Enable technical and non-technical teams and individuals to leverage data science and management, data, ML, and reliability engineering in an end-to-end fashion.

Do you want to contribute to our small, but growing services company with your knowledge of Data Engineering, Machine Learning, and Software Engineering? Within the hyped space of Data and AI, do you want to act as a thought leader and trusted advisor in the field of DataOps & MLOps?

We are looking for a Data & MLOps Engineer who will be involved in the whole lifecycle of projects, both internally and externally:

• Consulting, Engineering & Training: You perceive data, software, and machine learning 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, ML/LLM Integrations, Reliability Engineering & AI/ML Operationalization: You understand how to successfully deliver data and machine learning projects from the prototype or pilot phase into production, integrate and test software and models, 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 ‘Digital Highway for Data & ML systems’).
• Expertise & Thought Leadership: You strive to become an expert and a trusted advisor in the field of DataOps and MLOps
• 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.

Professional experience (minimum 3 years) as a Data, Machine Learning, or Software Engineer.

Experience with and, ideally, certification(s) in major data and AI platforms (e.g. Snowflake, Databricks, Dataiku).

Familiarity with DataOps, DevOps, and MLOps best practices, as well as topics such as Data Mesh, Data Lake/Warehouses, and Reliability Engineering.

Familiarity with data engineering, ML, and Generative AI models, frameworks & tools.

Understanding and strong interest in the end-to-end life cycle of projects, code, model, and data pipelines, and working with various stakeholders.

• Technical, hands-on experience with at least some of the following:
• Programming languages
• Distributed systems (Hadoop, Spark) and data structures.
• SQL and NoSQL databases.
• Cloud Services.
• REST API and microservices.
• Docker and knowledge of Kubernetes.
• Agile development methods and CI/CD.

Experience working in a client-facing or consulting role.

Fluency in  German and English (written and spoken).Swiss passport or a valid EU/EFTA work permit.

• A young and dynamic services company with an experienced, knowledgeable, and passionate team.
• An entrepreneurial environment and the chance to have a real impact on the company’s development and growth.
• Work on cutting-edge data, AI, and analytics topics that have a real impact across industries.
• A culture that is both performance-oriented and customer-driven and at the same time team-oriented, friendly, and supportive, incl. regular knowledge-sharing sessions and team events
• A hybrid working model with flexibility as long as both client (of which some may require onsite presence) and internal commitments (i.e., one team office day per week) are met.