Lead the design, implementation, and ongoing management of a multi-cloud environment spanning Google Cloud, Azure, and AWS.
Develop a scalable, self-service platform layer that empowers engineering teams to deploy and manage infrastructure efficiently.
Design and implement networking solutions connecting cloud and edge environments, ensuring secure, reliable, and scalable communication.
Enforce security best practices and compliance standards including ISO 27001, zero-trust models, and software supply chain protections.
Implement comprehensive observability with metrics, logging, distributed tracing, and proactive alerting across all systems.
Build and refine the MLOps pipeline, supporting data workflows, GPU-intensive workloads, and model deployment for AI systems.
Ensure consistent reliability and performance of distributed systems deployed across multiple geographic regions.
Collaborate directly with executive and technical leadership on infrastructure strategy, architectural direction, and long-term planning.

Minimum of five years in DevOps, infrastructure, or platform engineering with direct responsibility for production environments.
Demonstrated experience designing and managing complex cloud or hybrid infrastructure across AWS, GCP, or Azure.
Proficiency with Kubernetes and Infrastructure-as-Code tools such as Terraform for automated provisioning.
Solid knowledge of networking principles and cloud networking including routing, DNS, VPNs, and security protocols.
Experience with identity and access management systems including IAM, RBAC, and federated identity via OIDC or SAML.
Strong programming ability in Python or Go, with a focus on building robust, maintainable systems.
Production experience with CI/CD pipelines and GitOps methodologies for infrastructure and application delivery.
In-depth understanding of Linux operating systems and distributed computing concepts.
Familiarity with event-driven architectures and API technologies such as Kafka, AMQP, REST, or gRPC.
Knowledge of modern operational practices including Site Reliability Engineering, DevSecOps, and platform engineering principles.
Hands-on experience with MLOps tools or a strong drive to rapidly develop expertise in machine learning operations.

Experience using configuration management tools such as Ansible.
Understanding of distributed storage and data systems.
Background working with on-premises or edge computing infrastructure.
Track record in incident management, defining SLOs, and improving system reliability.

Significant ownership over system design and architectural decisions, not just maintenance.
Rapid professional growth through exposure to multi-cloud platforms, edge computing, and AI infrastructure.
Access to cutting-edge technologies including MLOps pipelines, GPU orchestration, and distributed edge systems.
Work in a high-impact environment where infrastructure choices directly affect large-scale production systems.
Collaborate with seasoned engineers from leading technology companies who embrace DevOps culture.
Clear trajectory for advancement into senior technical roles such as Staff Engineer, Architect, or leadership positions.

5+ years of experience in DevOps / infrastructure / platform engineering, with clear ownership of production systems.
Proven ability to design and operate complex cloud or hybrid architectures (AWS, GCP, Azure).
Strong Kubernetes and Infrastructure-as-Code expertise (Terraform or similar is expected).
Deep understanding of networking fundamentals and cloud networking (routing, VPNs, DNS, security layers).
Solid grasp of identity and access systems (IAM, OIDC/SAML, RBAC).
Strong programming skills (Python and/or Go) — you build real systems, not just glue scripts.
Experience with CI/CD and GitOps workflows in production environments.
Strong Linux and distributed systems knowledge.
Exposure to event-driven systems and APIs (Kafka, AMQP, REST, gRPC).
Good understanding of modern infrastructure disciplines (SRE, DevSecOps, platform engineering).
Working knowledge of MLOps or strong motivation to grow into it quickly.

Real ownership: You won’t just maintain systems — you’ll design and shape them.
Steep growth curve: Work across multi-cloud, edge computing, and modern AI infrastructure.
Cutting-edge tech: MLOps, GPU orchestration, distributed edge systems, and emerging AI workflows.
High-impact environment: Your decisions directly influence production systems at scale.
Strong team: Work with experienced engineers from top-tier companies who live DevOps philosophy.
Clear growth path: Opportunity to grow into Staff Engineer, Architect, or technical leadership roles.

Nomitri is hiring a Senior DevOps/MLOps at fast growing Berlin AI Company