Job Description
Do you get excited about scaling machine learning systems in production?
Are you ready to work on cutting-edge Computer Vision technology?
Do you feel tired of working in a big company?
Would you like to cooperate with top professionals in our industry?
If your answers are mostly yes, then you should keep reading. At Nomagic, we’re on a mission to teach robots the real world.
We’re now looking for a Senior DevOps Engineer to own and scale the infrastructure behind our Computer Vision ML Cloud Service.
Offer essentials:
- Work on cloud-native ML infrastructure at scale
- Salary: 26,000 - 30,000 gross UoP per month
- Equity for every employee
- Relocation package
- No late evening calls - the entire eng team is based in Europe :)
- English-speaking environment
- We work mainly remotely, but you have to reckon with occasional office visits if the task requires it
Here is why we love this job ourselves, and hope you will enjoy it too:
- We get to be creative
- We’re still pretty small, so everyone has a direct impact on the final result
- Nothing is written in stone, we can easily change the technology we use (if the requirements change)
- The CEO and part of the management are experienced infrastructure engineers who created the foundations of Google Cloud Platform
- We combine world-class research with top-notch engineering and apply it to solve real problems
Some of the problems you may try to solve with us:
- Scaling from single digit clients to 100+ in the future - designing multi-tenant infrastructure that scales”: single-digit clients źle brzmi… może samo “scaling infrastructure to hundreds of B2B customers
- Building standardized, repeatable deployment patterns across multiple customer environments
- Optimizing data pipelines for Computer Vision model training and deployment
- Building robust monitoring and alerting systems for ML service health and performance across all environments
- Automated CI/CD pipelines for ML model deployment with safe rollback strategies
- Deploying and managing applications on Kubernetes clusters using Helm charts and ArgoCD for GitOps workflows
- Infrastructure as Code using Terraform to provision and manage cloud resources consistently
- Implementing comprehensive observability with Prometheus and Grafana across multiple tenants
- Ensuring high availability, security, and cost optimization of cloud services
- Occasional hardware integration tasks (nice to have, not primary focus)
What skills we’d like you to have:
- 3+ years of experience as a DevOps or Infrastructure Engineer or in a similar role
- 2+ years of experience in software development
- Strong Kubernetes experience - managing production clusters, helm charts, and GitOps (Like ArgoCD)
- Infrastructure as Code expertise with Terraform
- Monitoring and alerting - hands-on experience with Prometheus, Grafana, and building effective alerting strategies
- Deep knowledge about Docker and container orchestration
- Experience with CI/CD pipelines and automated deployments
- Good understanding of networking and security in cloud environments
- Strong proficiency in Python
- Experience with one of the major cloud providers (preferably Google Cloud Platform)
- Experience designing multi-tenant infrastructure is a big plus
- Experience with ML infrastructure or data-intensive applications is a plus
- Hardware experience (servers, edge devices) is nice to have but not required
- Fluent communication in English
What should you expect once you apply?
- 30 minutes call with a Recruiter
- 45 minutes Hiring Manager interview
- 60 minutes Technical/Coding Interview
- Onsites - half a day of interviews & discussions at the office
See a short sneak peek of our product here:
https://vimeo.com/manage/videos/1051464045
https://vimeo.com/manage/videos/1051465112
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
