Senior DevOps Engineer (MLOps, Infrastructure)

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Location:

Job Category:

Other

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Job Reference:

1217b2c2e8d1

Job Views:

3

Posted:

04.04.2025

Expiry Date:

19.05.2025

Job Description:

Want to scale MLOps infrastructure and drive AI deployment at scale? Let’s build something great together!

Who needs your support?

noimos is a Zurich-based, but globally focused, start-up harnessing the potential of ML, computer vision and design to shake up the insurance industry and revolutionize the all-important claims process.

We are an independent subsidiary of one of the largest insurance groups in the world, AXA, enjoying many of the benefits that that brings.

From a personal point of view, noimos is a small group of experienced professionals, with strong background in technology, entrepreneurship, and insurance. We are passionate about making a difference with our products and enjoying ourselves along the way.

The challenges we tackle at noimos are diverse, demanding, and meaningful.

Shape Our MLOps Infrastructure

• Design, implement, and maintain MLOps infrastructure on GCP (Terraform, Pub/Sub, Vertex AI, CI/CD, GitHub Actions) to ensure scalability, automation, and reliability.
• Act as a Subject Matter Expert for MLOps and infrastructure, setting best practices and guiding the team in deploying and maintaining ML models in production.
• Build and optimize CI/CD pipelines for efficient training, serving, and monitoring of ML models, ensuring seamless operations at scale.
• Work within the ML engineering team, focusing on productionizing ML models, optimizing inference pipelines, and ensuring long-term model stability.
• Serve as the bridge between the ML team and the software development team, ensuring smooth integration of infrastructure and ML workflows.
• Take part in core ML engineering tasks, such as managing model inference pipelines, monitoring performance, and ensuring model stability in production.
• Contribute hands-on with Python development, focusing on automation, infrastructure, and tooling.

Your Toolkit for MLOps Mastery

• You have 2+ years of experience with DevOps and MLOps concepts, ideally using GitHub workflows, with a strong focus on infrastructure scalability and automation.
• Furthermore, you gained 5+ years of experience in data science or software engineering projects, with a particular focus on Deep Learning and Computer Vision, including deploying and maintaining ML models in production.
• You enrich your team with strong programming skills in Python, incl. clean/modular and production-ready code.
• You are experienced in working with large data sets and in building auto-scaling ML systems & MLOps.
• Ability to guide and mentor teams, assign tasks, and foster an environment of collaboration and efficiency.
• Ideally, you are proficient at using GCP (including Pub/Sub, Vertex), CI/CD practices, GitHub Actions, Terraform, and pipeline automation.
• You are proficient in English.

Typical office hours with flexibility

Bike room and showers

Home office / remote working 2 days per week