AI Specialist

MDW group; Genf, Lausanne; Hybrid; CHF 120.000 - 190.000; Vollzeit

Benefits dieser Stelle

Hybrid working

30 days vacation

Training budget

Team-building events

Ownership across AI lifecycle

Zusammenfassung

MDW group is seeking an AI Engineer at specialist level for Switzerland, with a hybrid setup in Geneva or Lausanne (onsite 2 days per week in Lausanne). You will design, build and run production AI systems end to end, emphasizing LLM applications, RAG and agent-based architectures, turning research into tangible business value.

You will own the full lifecycle of AI services—from prototyping to monitoring—collaborating with client teams on site and ensuring code quality, reliability and

Qualifikationen

• Bachelor's degree in computer science or a related field.
• Hands-on experience building applications that integrate LLMs, RAG or AI agents.
• Strong Python and software engineering practice: testing, code review, version control and API design.
• Practical MLOps experience: containers, CI/CD, model serving and monitoring on a major cloud platform.
• Solid grounding in classical machine learning and statistics.
• Understanding of observability concepts (logging, monitoring, tracing, troubleshooting).
• Professional working proficiency in English.
• Must have legal authorization to work in Switzerland.
• Must be able to work on site at the client's office in Lausanne at least 2 days a week.

Aufgaben

• Design, develop and maintain production-grade AI applications and APIs, including RAG pipelines and agent-based workflows.
• Take models from prototypes to production: evaluation, deployment, monitoring, and cost and latency tuning.
• Build and maintain MLOps pipelines for data preparation, training, versioning and model CI/CD.
• Develop classical ML models where they beat an LLM on cost or reliability.
• Set up evaluation for LLM output: quality, hallucination rate, safety and regression testing.
• Collaborate with technical and business teams, and on site with the client, to define and implement solutions.
• Ensure code quality, performance and system reliability.
• Stay updated on emerging technologies and AI trends.

Kenntnisse

English proficiency

Communication

Team collaboration

Problem solving

Stakeholder management

Ausbildung

Bachelor's degree in computer science or related field

MSc in Data Science, Machine Learning or related field

Tools

Python

APIs

CI/CD

Containers

Model serving

Monitoring tools

Version control

Vector databases

Azure

Databricks

Jobbeschreibung

Location: Geneva or Lausanne, Switzerland (hybrid: at least 2 days a week at the client's office in Lausanne)

Type: Full-time

Team: AI

About the Role

We're looking for an AI Engineer at Specialist level to design, build and run production AI systems end to end. The role leans toward LLM applications (RAG and agent-based architectures), with classical machine learning and Python backend services when a use case needs them.

You'll own the full lifecycle of AI services (prototyping, evaluation, deployment, monitoring and maintenance), with a strong focus on practical solutions that run reliably in production and turn current research into real business value.

This is a hybrid position based in Geneva or Lausanne, with at least 2 days a week at the client's office in Lausanne. It's ideal for someone who enjoys simplifying complexity, learning fast and shipping tools that work.

Core Tech Stack

• Machine Learning: classical ML and statistics (forecasting, classification, anomaly detection)
• Backend (primary): Python, APIs
• MLOps: model versioning, serving, evaluation and monitoring
• Observability: logging, monitoring, tracing, LLM output evaluation

Key Responsibilities

• Design, develop and maintain production-grade AI applications and APIs, including RAG pipelines and agent-based workflows.
• Take models from prototypes to production: evaluation, deployment, monitoring, and cost and latency tuning.
• Build and maintain MLOps pipelines for data preparation, training, versioning and model CI/CD.
• Develop classical ML models where they beat an LLM on cost or reliability.
• Set up evaluation for LLM output: quality, hallucination rate, safety and regression testing.
• Collaborate with technical and business teams, and on site with the client, to define and implement solutions.
• Ensure code quality, performance and system reliability.
• Stay updated on emerging technologies and AI trends.

Requirements

• Up to 5 years of professional experience in AI engineering, machine learning or data science.
• Hands-on experience building applications that integrate LLMs, RAG or AI agents.
• Strong Python and software engineering practice: testing, code review, version control and API design.
• Practical MLOps experience: containers, CI/CD, model serving and monitoring on a major cloud platform.
• Solid grounding in classical machine learning and statistics.
• Understanding of observability concepts (logging, monitoring, tracing, troubleshooting).
• Bachelor's degree in computer science or a related field.
• Professional working proficiency in English.
• Must have legal authorization to work in Switzerland. We do not sponsor visas or work permits.
• Must be able to work on site at the client's office in Lausanne at least 2 days a week.
• Strong communication, organization and stakeholder management skills.

Nice to Have

• Professional working proficiency in French.
• Vector databases or vector search.
• Fine-tuning open-weight models.
• Experience in regulated or high-stakes sectors: finance or digital assets, energy, automotive or industrial.
• Research background or peer-reviewed publications in applied AI.
• Knowledge of quantum computing.
• MSc in Data Science, Machine Learning or a related field.
• Azure or Databricks certifications.

What We Offer

• Hybrid working in Geneva or Lausanne, with at least 2 days a week at the client's office in Lausanne.
• Paid vacation (30 working days)
• 3 team-building events per year.
• Dedicated budget for training and certifications.
• Hands-on work with Generative AI, RAG and agent-based architecture.
• Ownership across the full lifecycle of AI systems in production.
• Client-facing project work, where we aim to keep stable teams together across projects whenever possible.