Data & MLOps Engineer (Experienced)
Machine Learning Architects Basel (MLAB)
- Bâle
- CDI
- Temps-plein
- 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.
- 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.
- 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.
- 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.