Senior Platform Engineer (Kubernetes & Data Infrastructure)

Descrição do trabalho

About Sybilion

Sybilion builds AI-driven market forecasting for process industries (chemicals, packaging, pulp & paper, textiles, and broader manufacturing). We help procurement, supply chain, and commercial teams make better buy/sell decisions by turning messy external signals and internal operational data into clear, defensible forecasts that teams trust and act on.

Our stack includes Python-based microservices, PostgreSQL data infrastructure, and ML/AI workflows that support forecasting models and decision tooling.

About the Role

We’re hiring someone to own both our platform and data infrastructure: Kubernetes administration, Linux systems, CI/CD, observability, and PostgreSQL administration for our data lakes and ML pipelines. You’ll keep production reliable, fast, secure, and scalable, while supporting the day-to-day needs of our engineers and ML workflows.

This is an on-site role in Maia (Porto). We value in-person collaboration and move quickly.

What You’ll Do

• Platform / Kubernetes / Systems
• Design, deploy, and operate Kubernetes clusters in production (networking, storage, security)
• Operate Linux server infrastructure (Ubuntu/RHEL), patching, hardening, and reliability
• Manage Docker image lifecycle (builds, optimisation, registry management, security scanning)
• Implement and maintain CI/CD pipelines for microservices deployments and infrastructure changes
• Build and maintain Infrastructure as Code (Terraform, Ansible, Helm) and Git workflows
• Operate and improve monitoring, logging, and alerting (Prometheus/Grafana, ELK/EFK/Loki, etc.)
• Manage secrets and credentials securely (Vault, Sealed Secrets, or equivalent)
• Ensure high availability, capacity planning, incident response, and disaster recovery readiness
• Support GPU-enabled workloads and ML/LLM deployments (resource allocation, utilisation, scaling)

• PostgreSQL / Data Infrastructure
• Administer and optimise PostgreSQL databases and data lake infrastructure (performance, reliability, cost)
• Own backup/recovery and disaster recovery procedures (including point-in-time recovery)
• Design schemas, indexing strategies, and query optimisation approaches; analyse execution plans
• Manage migrations and versioning (schema changes, rollout strategies, rollback plans)
• Implement replication/failover/clustering patterns for high availability
• Own database security: access controls, encryption at rest/in transit, audit logging, compliance needs

• Python Microservices / Data Pipelines / ML Workflows
• Support deployment and troubleshooting of Python microservices (FastAPI/Flask/Django or similar)
• Help maintain Python environments and dependency management (pip/poetry/conda/mamba)
• Support ETL/ELT pipelines feeding our data lake and ML training workflows
• Implement data quality checks and validation where needed
• Partner with engineers and ML team to improve runtime performance, reliability, and operational visibility

• Must-Have Experience (Required)
• 5+ years of hands-on production experience in: Linux, Docker, Kubernetes, and PostgreSQL
• Strong Kubernetes administration skills (clusters, networking, ingress, storage, RBAC, security)
• Strong PostgreSQL administration skills (performance tuning, backups, replication/HA, security)
• Strong Linux systems skills (operations, troubleshooting, hardening)
• CI/CD experience (GitHub Actions/GitLab CI/Jenkins or similar)
• Infrastructure as Code experience (Terraform and/or Ansible; Helm for Kubernetes)
• Observability experience (metrics, logs, alerting; root-cause analysis)
• Solid Python literacy for debugging services and automating operational tasks
• Strong communication skills in English and comfort working independently end-to-end
• Willingness to participate in an on-call rotation for critical systems

• Preferred (Nice to Have)
• Startup background (you’ve worked in small teams, moved fast, and owned outcomes end-to-end)
• Experience running ML infrastructure (MLflow, Kubeflow, Airflow, KServe/TorchServe, etc.)
• GPU cluster experience (NVIDIA GPU Operator or similar) and model serving optimisation
• Experience with service mesh (Istio/Linkerd)
• Experience with cloud managed databases (AWS RDS, GCP Cloud SQL, Azure Database)
• Familiarity with data lake / warehouse patterns and data versioning (DVC/MLflow tracking)
• Experience with Redis/MongoDB or other complementary data systems

• Soft Skills We Value
• Strong problem-solving and analytical mindset
• Calm, structured incident handling and good judgement under pressure
• Proactive improvement orientation (you spot issues before they become outage