Senior MLOps Engineer (Infrastructure Expert)
Infos sur l'emploi
- Date de publication :07 mars 2025
- Taux d'activité :80 – 100%
- Lieu de travail :Zürich
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.
Our tech stack includes:
- Infrastructure (GCP, Vertex AI, Cloud Run, Terraform, GPU, Docker, GitHub Actions, CI/CD)
- Python and computer vision libraries (e.g. NumPy, pandas, OpenCV, scikit-image)
- Frameworks associated with training and evaluation of image recognition tasks (PyTorch)
- Databases (SQL, BigQuery)
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.
