AI Summary
Join a well-funded AI software startup near Porto as a Senior Cloud Engineer to design, deploy, and manage production-grade Kubernetes clusters for ML and microservice workloads. With 3-7+ years of experience in Kubernetes administration, database management, and cloud platforms, you'll shape the backbone of forecasting systems used by billion-euro industrial clients.
Key Highlights
Design, deploy, and manage production-grade Kubernetes clusters
Maintain and optimize container orchestration
Oversee CI/CD pipelines using tools like GitHub Actions, GitLab CI, and Terraform
Technical Skills Required
Benefits & Perks
Competitive Salary
Remote Work
Relocation Support
Training & Growth Budget
Daily Meal Subsidy
Team Events
Onboarding Buddy
Job Description
Senior Cloud Engineer
At a well-funded AI software startup near Porto, a dedicated team of engineers and researchers is rethinking industrial forecasting. With a clear B2B product and growing client base across Europe and Asia, this company is scaling its ML-powered infrastructure for real-world decision-making. You’ll join a tight-knit group of 13 people who combine startup agility with deep expertise in data science, cloud infrastructure, and industrial processes. The office is located in Vila Nova da Telha, and the working language is English. Their goal? Build systems that actually get used in procurement and production, not slide decks. Would you join as Senior Cloud Engineer?
Your Benefits As Senior Cloud Engineer
- Competitive Salary: Commensurate with your experience and contributions.
- Remote Work: On site presence in Porto is expected during the initial 1 to 2 years to work closely with the full team and establish strong collaboration. After this phase, full remote work is an option.
- Relocation Support: For your on-site onboarding or if you decide to move to Porto, you receive support with logistics, housing and onboarding connections to make it smooth.
- Training & Growth Budget: Set aside for conferences, courses, and certifications.
- As a DevOps & Kubernetes Engineer: You’ll have a direct impact on critical ML infrastructure and shape the backbone of forecasting systems used by billion-euro industrial clients.
- Daily Meal Subsidy: Enjoy lunch on the company when working from the office.
- Team Events: From BBQs to game nights and a Christmas party, with the first drinks on the house.
- Onboarding Buddy: You won’t be left alone—get paired with someone who helps you ramp up quickly.
- Design, deploy, and manage production-grade Kubernetes clusters for ML and microservice workloads
- Maintain and optimize container orchestration, including service mesh, network policies, and resource allocation
- Oversee CI/CD pipelines using tools like GitHub Actions, GitLab CI, and Terraform
- Manage Docker image lifecycle and enforce security best practices
- Monitor infrastructure health using Prometheus, Grafana, and centralized logging solutions
- Collaborate on Infrastructure as Code (IaC) and ensure scalable, reproducible deployments
- Support GPU-based workloads and optimize GPU resource utilization for LLM agents
- Maintain Linux-based cloud servers, implement security protocols, and manage DNS, VPNs, and firewalls
- Troubleshoot Python microservices and contribute to automation and monitoring setups
- Implement model serving and orchestration pipelines (MLflow, Kubeflow, etc.)
- Ensure high availability and disaster recovery strategies across systems
- 3-7+ years of advanced hands-on experience with Kubernetes administration, including networking, storage, and security
- Good experience with database & data management especially with PostgreSQL
- Proficient in Docker, multi-stage builds, and image lifecycle management
- Strong Linux system administration skills (Ubuntu or RHEL-based systems)
- Experience with cloud platforms, ideally Google Cloud Platform (GCP)
- Solid understanding of CI/CD pipelines using tools like GitHub Actions, GitLab CI, or Jenkins
- Familiarity with Infrastructure as Code (Terraform, Ansible) and GitOps workflows
- Confident in managing GPU workloads and ML/LLM-serving infrastructure
- Experience with monitoring and observability tools such as Prometheus, Grafana, ELK/EFK stack
- Comfortable with Python microservices and ML workflow troubleshooting
- Fluent English skils at C1 or above
- As a DevOps & Kubernetes Engineer, you bring a proactive mindset, technical depth, and readiness to take ownership in a fast-moving AI environment
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