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 endtoend data/ML/LLM systems.
• Enable technical and nontechnical teams and individuals to leverage data science and management data ML and reliability engineering in an endtoend fashion.

Tasks

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 bestfit 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 clients teams.

Requirements

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 endtoend life cycle of projects code model and data pipelines and working with various stakeholders.

• Technical handson 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 clientfacing or consulting role.

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

Benefits

• 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 companys development and growth.
• Work on cuttingedge data AI and analytics topics that have a real impact across industries.
• A culture that is both performanceoriented and customerdriven and at the same time teamoriented friendly and supportive incl. regular knowledgesharing 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.