AI Platform Engineer / MLOps

Location: Zurich

Employment: Permanent Full-Time

Building the production layer behind real world AI systems

Artificial intelligence often gets described through models, research and data science. In reality, the most difficult part is making those models work reliably in production.

A highly specialised technology company is building the next generation of AI driven platforms used by financial institutions and regulated organisations around the world. Their systems process complex data environments where reliability, security and performance are essential. AI models are only valuable if they can be fully trusted by being trained, deployed and operated safely at scale.

We are looking for an AI Platform Engineer who will build the production layer that sits between model engineering and infrastructure. You will work closely with AI engineers who build the models, while partnering with TechOps teams that operate the platform environment. Your responsibility is to ensure models move smoothly from development into stable, scalable and observable production systems.

The focus of this role is not research or model design. It is about building the engineering systems that allow AI to function as reliable software.

You will design and maintain the services, pipelines and deployment processes that allow models to train, deploy and serve predictions in a controlled and monitored environment.

You will play a central role in building and maintaining the platform that powers AI models in production environments. This includes:

• Building and maintaining APIs that allow models to run inference and serve predictions

• Designing CI/CD pipelines that automate the training, validation and deployment of models

• Managing the lifecycle of models through tools such as MLFlow

• Building data and training pipelines using Spark based workflows

• Ensuring robust monitoring, logging and observability across the AI platform

• Maintaining strong testing standards and deployment controls for model updates

• Collaborating closely with AI engineers and platform infrastructure teams

Your work will ensure that AI systems remain reliable, secure and scalable across complex enterprise environments.

This role requires an engineer who enjoys building robust systems around machine learning rather than focusing on model development itself. You will likely have experience building ML or data platforms and designing the systems that allow models to run in production with typical backgrounds include:

• Machine Learning Platform Engineering

• MLOps Engineering

• AI Infrastructure or ML Platform Development

• Data Platform Engineering with strong ML pipeline exposure

You should bring experience across several of the following areas:

• Strong Python engineering experience

• Building and operating ML pipelines and model lifecycle workflows

• Experience with Spark based data or training pipelines

• Experience with MLFlow or similar model lifecycle platforms

• CI/CD pipelines for machine learning systems

• Building REST APIs for model inference and services

• Monitoring and logging of distributed systems

Experience working with secure or regulated environments is helpful but not required.

This role sits at the heart of a modern AI platform. You will be building the systems that allow advanced models to operate reliably in real world production environments where performance, stability and security matter. The engineering challenges involve distributed data processing, scalable model serving, automation of model lifecycle management and designing robust deployment pipelines.

Location: Zurich with 2-3 days per week in the office