AI & MLOps Engineering Consultant (Experienced, German-speaking)

Machine Learning Architects Basel (MLAB); Basel; Ibrido; CHF 110.000 - 150.000; Tempo pieno

Vantaggi offerti da questo lavoro

Young team

Entrepreneurial environment

Cutting-edge topics

Supportive culture

Hybrid work model

Descrizione del lavoro

Machine Learning Architects Basel (MLAB) is seeking an experienced AI & MLOps Engineering Consultant who will drive client projects from concept to production. You will work in a German-speaking capacity, combining consulting with hands-on engineering across data pipelines, model deployments, and reliability engineering.

The role emphasizes collaboration with client teams, training, and knowledge sharing, while delivering scalable, measurable AI and data solutions in a hybrid Basel environment.

Competenze

• 3+ years of professional experience in ML, AI or software engineering.
• Experience with data and ML platforms (Snowflake, Databricks, Dataiku, IBM Watson).
• Knowledge of DataOps, DevOps and MLOps best practices.
• Experience with data engineering, ML and Generative AI models.
• Understanding end-to-end lifecycle of data and ML pipelines.

Mansioni

• Consulting, Engineering & Training to translate client needs into deliverables.
• Requirement analysis to define best-fit AI solutions.
• Implement data pipelines, ML/LLM integrations, and reliability engineering.
• Develop concepts and blueprints for data & ML systems.
• Be a thought leader, sharing knowledge and coordinating with client teams.

Conoscenze

Programming languages

Distributed systems

SQL/NoSQL databases

Cloud services

REST and microservices

Docker

Kubernetes

CI/CD & Agile

Descrizione del lavoro

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. 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, LLMs, and other GenAI solutions.
• Design, test, integrate and operate data, model and code pipelines, and end-to-end data/ML/LLM systems (DataOps, MLOps & DevOps).
• 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.

Tasks

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 AI Engineering?

We are looking for an experienced and German-speaking AI & MLOps Engineering Consultant 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 AI Engineering 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.

Requirements

Professional experience (minimum 3 years) as a Machine Learning, AI or Software Engineer focusing on data and ML systems.

Experience with and, ideally, certified in major data and AI platforms (e.g., Snowflake, Databricks, Dataiku, IBM Watson).

Familiarity with DataOps, DevOps, and MLOps best practices and 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 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 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 most require onsite presence) and internal commitments (i.e., one team office day per week) are met.

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