Senior Machine Learning Engineer

Function Description / Professional Tasks

You'll join the internal Data & AI Engineering platform team, responsible for the tooling and components that enable data and AI use cases across the company. The team builds on top of existing cloud infrastructure - owning the data and AI layer: reusable modules, lifecycle tooling, agentic components, and the internal developer experience for teams building on the platform.

You'll contribute to design, own implementation, and work directly with internal teams that consume what you build.

What you can do

Building and maintaining reusable platform components: Terraform modules, deployment blueprints, and abstractions that internal ML and GenAI teams depend on

ML lifecycle tooling: experiment tracking, model registry, deployment pipelines, monitoring

GenAI-specific components: LLM access and routing, agent lifecycle and versioning, tool integrations, guardrails, evaluations

CI/CD for ML and GenAI workloads

Supporting internal teams in adopting platform components - documentation, examples, hands‑on guidance where needed

Personal Skills and Education

What you bring

You have more than 5 years of proven experience working with AI.

Solid engineering fundamentals – maintainable code, clear system design thinking, ability to own deliverables end‑to‑end

Cloud platform experience (AWS, GCP, Azure, or Databricks).

Infrastructure‑as‑code experience; Terraform exposure is a plus

Python proficiency

Experience building and maintaining ML workflows end‑to‑end – training pipelines, experiment tracking, model registry, deployment, and monitoring.

Deploying and maintaining AI Solutions in Kubernetes, preferably in EKS.

Familiarity with relevant tooling (e.g. MLflow, SageMaker Pipelines, Kubeflow, or similar).

Hands‑on experience with LLMs or GenAI tooling in a technical context – working with model APIs, building or operating agentic systems, or contributing to GenAI infrastructure (evaluation, observability, guardrails, etc.)

Knowledge and experience with GPU utilization optimization (e.g. vLLM) is a plus

Education

University/technical college degree in computer science or comparable.

Language Skills

English oral and written

Optional: German oral (and written)

Additional Comments

Willingness to travel

Hybrid model: One day a week @office (mandatory)

Why us?

Open minded company where every employee has to contribute to the development of the company - ideas are welcome as well as independent thinking to

25 annual days of vacations

Flexible working hours

Annual allowance for Benefits (Training, Gym, Public Transportation, Technologies, etc...)

An amazing onboarding week in Switzerland

iPhone

Second screen to work at home (flat or a curved one)

First month tech allowance to buy your headset or, if you already have one, whatever you need to work comfortably

Health insurance for you and your family

Life insurance

Office perks (coffee, fruit, stand up desks, etc...)

So much more...

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