MAIA

Platform Engineer

• Take technical ownership of infrastructure and operations as the first dedicated Senior DevOps / Platform Engineer
• Evolve the existing platform architecture, automation, and operational standards
• Operate self-administered Linux systems on European VMs and dedicated servers
• Integrate AWS, Azure, and GCP services
• Operate and further develop Docker Compose, networks, reverse proxies, and API gateways
• Develop Infrastructure as Code, CI/CD, and deployment workflows
• Implement automated checks, traceable configurations, and tested rollback procedures
• Take responsibility for PostgreSQL in production
• Analyze and optimize performance, connection pooling, and capacity
• Set up and validate reliable backup and restore procedures
• Further develop the self-hosted observability stack with metrics, logs, traces, and actionable alerting
• Implement least privilege, secrets management, vulnerability scanning, and patch management
• Implement technical measures and automated evidence collection for the ISO 27001 initiative
• Evaluate inference providers and operating models based on latency, throughput, cost, and reliability
• Build and operate an in-house LLM serving environment on GPU infrastructure over time
• Automate recurring operational tasks and document operating procedures
• Systematically investigate incidents and implement lasting improvements
• Collaborate with Engineering and the executive team without people-management responsibilities

Senior DevOps / Platform Engineer, AI Infrastructure

• Take technical ownership of MAIA’s production infrastructure
• Operate and evolve self-administered Linux systems across virtual machines and dedicated servers
• Manage containers, networks, reverse proxies, and API gateways
• Improve Infrastructure as Code, GitHub Actions workflows, deployment processes, automated checks, versioning, and rollback capabilities
• Make deployments safe, repeatable, and easy for engineers to use
• Operate and improve PostgreSQL in production, including performance analysis, connection pooling, capacity planning, backups, and tested restore procedures
• Develop the self-hosted observability stack connecting metrics, logs, traces, and actionable alerts
• Strengthen infrastructure security through IAM, least privilege, secrets management, TLS, vulnerability scanning, and patch management
• Implement technical controls for ISO 27001 with continuously generated, understandable, and auditable evidence
• Shape AI infrastructure by integrating and evaluating model and inference providers
• Evaluate providers based on reliability, latency, throughput, cost, and operational effort
• Explore self-hosted LLM inference, potentially including GPU infrastructure and vLLM
• Improve incident response and operational resilience through root-cause investigation, runbooks, documentation, and lasting improvements
• Improve internal developer experience by reducing manual work and creating clear interfaces and workflows
• Make infrastructure and inference costs transparent and inform build, buy, and hosting decisions
• Assess the existing platform, identify risks, explain options, and take improvements through to reliable production operation
• Collaborate with software engineers, the CTO, company leadership, and distributed team members
• Work as a hands-on senior individual contributor without people management