Full-Stack Engineer: AI/ML Platforms & CloudOps

Cartier; Glanewiler; On-site; CHF 90,000 - 130,000; Full time

Job summary

Cartier is seeking a Software Engineer to drive end-to-end software development, connecting AI and data capabilities with business value. You will design scalable backend infrastructure, create functional UIs, and ensure compliant, secure delivery across the organization.

The role emphasizes API design, microservices, and DevOps practices on Google Cloud, with collaboration across data science and engineering teams to productionize models and pipelines.

Qualifications

• Master’s degree in Computer Science or related field.
• 3–5 years of full-stack or backend development experience.
• Proficiency in Python and FastAPI, plus frontend basics (HTML, CSS, JS).

Responsibilities

• End-to-End Application Delivery: design and develop software solutions across server-side and client-side components.
• API & Microservices Architecture: build REST APIs and microservices to expose data and ML capabilities.
• Organizational Collaboration: work with Data Scientists and Data Engineers to productionize models and data pipelines.
• Platform Operations & DevOps: maintain CI/CD pipelines and Google Cloud infrastructure.
• Compliance & Governance: ensure software adheres to group standards and AI regulations.
• Operational Excellence: create templates and reusable libraries to accelerate Time-To-Market.

Skills

Python

REST APIs

Microservices

Backend development

DevOps

Team collaboration

Education

Master’s degree in Computer Science or related field

Tools

Google Cloud Platform

Cloud Run

GKE

CI/CD

Containerization

Job description

Cartier is seeking a Software Engineer to drive end-to-end software development, connecting AI and data capabilities with business value. You will design scalable backend infrastructure, create functional UIs, and ensure compliant, secure delivery across the organization.

The role emphasizes API design, microservices, and DevOps practices on Google Cloud, with collaboration across data science and engineering teams to productionize models and pipelines.