Staff Site Reliability Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Site Reliability Engineer based in Germany.

This is a fully remote opportunity for a highly experienced SRE leader to shape reliability across complex, AI-driven production environments.

You will act as a senior technical authority, designing resilient infrastructure and establishing SRE practices that scale with rapid business growth.

The role spans cloud infrastructure, Kubernetes, observability, CI/CD, data platforms, and machine learning systems.

You’ll work across platform, product, data, and ML engineering teams to improve availability, performance, security, and operational efficiency.

A key focus will be productionizing AI workloads, standardizing customer environments, and strengthening infrastructure for enterprise-scale deployments.

You’ll tackle complex reliability challenges hands-on while influencing architecture and technology direction across engineering.

The environment values ownership, technical excellence, automation, continuous improvement, and the ability to influence without relying on formal authority.

• Accountabilities
• Architect, deploy, operate, and continuously improve scalable, secure production environments, with a strong preference for AWS-based infrastructure.
• Lead reliability initiatives across multiple engineering streams and establish consistent SRE practices throughout the organization.
• Design, evolve, migrate, and optimize Kubernetes-based infrastructure, including production hardening and scaling.
• Establish and enforce robust Infrastructure-as-Code standards using Terraform or equivalent technologies.
• Define, implement, and operationalize SLIs, SLOs, error budgets, and other reliability practices.
• Strengthen observability across applications, infrastructure, data pipelines, and ML systems to improve visibility into system health and performance.
• Collaborate with product and data teams to incorporate product telemetry, model analytics, and operational data into reliability insights.
• Design and optimize CI/CD pipelines across the complete software lifecycle, from build and testing through deployment and rollback.
• Improve release safety, deployment frequency, operational predictability, and adherence to service-level objectives.
• Lead incident response for complex, cross-system failures and drive thorough post-incident reviews and corrective actions.
• Reduce operational toil through automation, platform engineering, and improved tooling and processes.
• Design scalable processes and infrastructure for absorbing, standardizing, monitoring, and troubleshooting customer environments.
• Support and productionize ML workloads by implementing MLOps practices for model deployment, monitoring, and retraining workflows.
• Ensure infrastructure and operational practices meet enterprise-grade security, compliance, and regulatory requirements.
• Mentor engineers, share best practices, and raise the overall reliability and engineering standards across teams.
• Collaborate with Staff Engineers and Architects to influence global product architecture and long-term technology strategy.

Requirements

• Extensive hands-on experience in Site Reliability Engineering, Production Engineering, or a closely related infrastructure role.
• Proven experience establishing or scaling SRE practices within high-growth, complex, or highly distributed technical environments.
• Deep expertise with AWS or Azure cloud infrastructure and modern cloud-native architectures.
• Strong production experience with Kubernetes, including migration, scaling, optimization, and security hardening.
• Advanced Infrastructure-as-Code expertise using Terraform or an equivalent technology.
• Demonstrated experience designing, implementing, and optimizing end-to-end CI/CD pipelines.
• Strong knowledge of observability practices and tooling across distributed applications and infrastructure.
• Experience troubleshooting complex multi-tenant, customer-hosted, or enterprise environments.
• Experience supporting production data platforms and machine learning systems.
• Practical MLOps experience, including model deployment, monitoring, and operational lifecycle management.
• Strong understanding of distributed systems, scalability, resilience, fault tolerance, and failure modes.
• Ability to think across systems and understand the interactions between infrastructure, applications, data, and ML workloads.
• Strong communication and collaboration skills, with the ability to work effectively across engineering and business functions.
• Experience with large-scale global B2B or B2C products is desirable.
• Experience working with AI/ML, NLP, or LLM-based products is a strong advantage.
• Familiarity with integrating product analytics and model performance metrics into operational monitoring is beneficial.
• Experience operating in enterprise environments with stringent security, compliance, and regulatory requirements is preferred.
• Experience implementing regulatory controls within cloud infrastructure is a plus.
• Experience scaling infrastructure during periods of rapid growth is advantageous.
• Experience evaluating infrastructure tools, platforms, and vendors is desirable.
• Experience deploying and operating solutions within large enterprise customer accounts or VPCs is a strong plus.
• Strong problem-solving skills, high ownership, and accountability.
• Ability to anticipate failure modes, operate across multiple engineering streams, and influence technical decisions without formal authority.
• Continuous-learning mindset with a strong commitment to improving systems, processes, and engineering practices.

Benefits

• Full-time, permanent employment.
• Fully remote position within European time zones.
• Opportunity to work on infrastructure supporting advanced AI and agent-based workloads.
• Significant technical ownership and influence over reliability practices and architecture.
• Opportunity to work across cloud infrastructure, Kubernetes, distributed systems, data platforms, and ML operations.
• Exposure to complex enterprise environments and large-scale production deployments.
• Collaboration with highly experienced engineers, architects, product teams, and AI/ML specialists.
• Opportunity to shape reliability standards and technology strategy as the organization scales.
• Strong culture of ownership, continuous improvement, and technical excellence.
• International and distributed working environment.
• Career growth and opportunities to expand technical leadership and organizational impact.

How Jobgether works

We use an

AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements.

Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company.

The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

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