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AI Software Engineer (GCP)

Visium SA

München · Hybrid Lead 2w ago

About the role

About Us

At Visium, we enable enterprise executives in defining their AI & Data strategy, execute large scale transformations and implement AI across operations, ensuring their organization becomes future-proof.

With expertise in strategy, architecture, cloud engineering, analytics, artificial intelligence and machine learning, we empower our clients to unleash and scale the power of their data.

We’re on a mission to pioneer a bright future and build future-proof and ethical organizations. Join the curious, the ambitious, the doers, the good-hearted, the ones who build a world we’re all in awe of – our Visiumees.

Ready to become one?

Role

We are looking for a Lead Software Engineer (Machine Learning focus) with strong experience in building production-grade systems and a solid exposure to MLOps and ML systems. The primary focus of this role is software development excellence: designing clean architectures, writing high-quality code, and operating reliable systems at scale. You'll also collaborate with business stakeholders to deliver scalable, maintainable, and high-impact ML solutions on Google Cloud Platform.

  • Design, develop, and maintain scalable, reliable software systems supporting ML use cases
  • Own production services end-to-end (design, implementation, testing, deployment, monitoring)
  • Apply and promote software engineering best practices (clean code, architecture patterns, testing, CI/CD)
  • Build and maintain APIs, services, and data pipelines used by ML systems
  • Implement MLOps workflows with a strong engineering mindset (automation, reproducibility, monitoring)
  • Design and operate cloud-native systems on Google Cloud Platform
  • Collaborate closely with ML engineers and data scientists to integrate models into production systems
  • Communicate clearly with stakeholders and contribute to technical decision-making

Requirements

To apply, this should be part of your background:

  • 6+ years of experience in software engineering
  • Very strong programming skills and proven experience building production systems
  • Solid background in software architecture, system design, and distributed systems
  • Experience applying engineering best practices in real-world environments
  • Hands-on experience with cloud platforms, with deep expertise in Google Cloud Platform (GCP)
  • Experience with CI/CD, infrastructure as code, and production monitoring
  • Strong communication and stakeholder management skills
  • Ability to work on-site in Winterthur once per week or if based outside of CH, availability to travel.

Benefits

What we offer:

  • A competitive compensation package
  • A yearly education budget to steep your learning curve
  • A yearly sport budget because a fit body leads to a fit mind
  • A flexible working culture because your work-life balance matters to us
  • A position that enables you to have an impact on 1,000s of people, and the whole company's growth.
  • An international, knowledgeable, and passionate team with a strong collaborative mindset

Check our LinkedIn and website to learn more about us & don’t hesitate to contact us if you have any questions.

Requirements

  • Very strong programming skills and proven experience building production systems
  • Solid background in software architecture, system design, and distributed systems
  • Experience applying engineering best practices in real-world environments
  • Hands-on experience with cloud platforms, with deep expertise in Google Cloud Platform (GCP)
  • Experience with CI/CD, infrastructure as code, and production monitoring
  • Strong communication and stakeholder management skills
  • Ability to work on-site in Winterthur once per week or if based outside of CH, availability to travel

Responsibilities

  • Design, develop, and maintain scalable, reliable software systems supporting ML use cases
  • Own production services end-to-end (design, implementation, testing, deployment, monitoring)
  • Apply and promote software engineering best practices (clean code, architecture patterns, testing, CI/CD)
  • Build and maintain APIs, services, and data pipelines used by ML systems
  • Implement MLOps workflows with a strong engineering mindset (automation, reproducibility, monitoring)
  • Design and operate cloud-native systems on Google Cloud Platform
  • Collaborate closely with ML engineers and data scientists to integrate models into production systems
  • Communicate clearly with stakeholders and contribute to technical decision-making

Benefits

education budgetsport budget

Skills

CI/CDGCPGoogle Cloud PlatformMLOpsPython

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