Data and AI Solutions Architect

vor 1 Monat

Basel, Basel-Stadt, Schweiz Machine Learning Architects Basel (MLAB) Vollzeit
About the Role

At Machine Learning Architects Basel (MLAB), we are seeking a highly skilled Data and AI Solutions Architect to join our team. As a key member of our team, you will be responsible for designing, building, and operating reliable data and machine learning solutions for our clients.

Your primary focus will be on helping our clients realize the full potential of data and AI solutions, from use case identification to data and ML platform implementation, integration, and testing operation of data products, ML models, and LLMs (DataOps & MLOps).

You will work closely with our team to design, test, integrate, and operate data, model, and code pipelines, and end-to-end data/ML/LLM systems. Your expertise will 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.

Key Responsibilities
  • Consulting, Engineering & Training: You will perceive data, software, and machine learning engineering as key capabilities for mastering the challenges of our clients' digital transformations, help them understand both their potential and their limitations, and deliver impactful, valuable services.
  • Requirement Analysis: You will analyze customer requirements and identify and define best-fit solutions.
  • Implementation of Data Pipelines, ML/LLM Integrations, Reliability Engineering & AI/ML Operationalization: You will 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 will contribute to our solution blueprints and concepts (e.g., our 'Digital Highway for Data & ML systems').
  • Expertise & Thought Leadership: You will strive to become an expert and a trusted advisor in the field of DataOps and MLOps
  • Ownership, Communication, Knowledge Sharing & Teamwork: You will 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.
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 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.

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 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.

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