This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Site Reliability Engineer based in Germany.
This is a senior reliability engineering role focused on defining and advancing the infrastructure standards behind a globally scaled, AI-native platform.
You will take ownership of reliability strategy across production infrastructure, with a particular focus on AWS, Kubernetes, event-driven systems, and AI agent workloads.
The role combines deep hands-on engineering with architectural leadership, incident management, observability, and technical mentorship.
You will design systems that remain resilient under increasing transaction volumes while establishing measurable standards for reliability across engineering teams.
A key part of the role will be evolving synchronous architectures toward durable asynchronous communication and strengthening the platform through resilience testing and chaos engineering.
You will also help shape how AI-assisted tools are used for automation, incident analysis, runbooks, and root-cause investigations.
Success means becoming the trusted technical authority for complex reliability decisions while creating practices that make reliability scalable across the organization.
Accountabilities
- Define and implement the reliability strategy across the platform, including SLOs, SLIs, error budgets, incident practices, and reliability standards adopted by engineering teams.
- Drive major architectural decisions as infrastructure evolves, evaluating technologies and designing systems that remain scalable, resilient, observable, and maintainable.
- Design and own event-driven communication and messaging infrastructure, including the transition from synchronous patterns to durable asynchronous architectures.
- Manage and evolve cloud infrastructure on AWS, using Infrastructure as Code to automate provisioning, configuration, deployment, and operational processes.
- Ensure Kubernetes and containerized workloads scale reliably as transaction volumes and AI workloads increase.
- Build and maintain comprehensive observability through monitoring, dashboards, alerting, application performance monitoring, and distributed tracing.
- Serve as the senior escalation point for complex production incidents, leading incident response, root-cause investigations, and blameless postmortems.
- Turn incident findings into permanent improvements through architectural changes, automation, operational controls, and resilience patterns.
- Establish a continuous chaos engineering and resilience testing practice through fault injection, game days, and controlled failure experiments.
- Mentor senior and mid-level engineers while raising the technical bar for reliability engineering and influencing engineering practices across teams.
- Use AI-assisted tooling for automation, runbooks, incident analysis, and root-cause investigations, while helping establish effective AI-enabled engineering practices.
- Within the first 6–12 months, establish the platform reliability strategy, lead at least one major architectural evolution, and drive adoption of the SLO and error-budget framework across engineering teams.
Requirements
- Extensive experience in Site Reliability Engineering, Platform Engineering, DevOps, or a closely related discipline, with demonstrated ownership of production-scale systems.
- Deep expertise in event-driven architecture and messaging systems such as Kafka, NATS, or RabbitMQ, including at-least-once delivery, consumer groups, dead-letter queues, backpressure, and migrations from synchronous to asynchronous architectures.
- Strong AWS expertise across services such as EC2, VPC, IAM, S3, and RDS, combined with solid networking fundamentals.
- Hands-on Infrastructure as Code experience using Terraform, Pulumi, or similar tools, with infrastructure managed through version-controlled workflows and code reviews.
- Strong production experience with Kubernetes and Docker, including container lifecycle management, resource limits, health checks, and orchestration at scale.
- Proven observability expertise using Datadog or equivalent platforms, including dashboards, monitoring, APM, distributed tracing, and alerting.
- Demonstrated experience defining and operating SLOs, SLIs, and error budgets across multiple services.
- Hands-on experience with chaos engineering, fault injection, game days, or resilience experiments using tools such as Gremlin, Chaos Mesh, AWS FIS, or similar technologies.
- Strong distributed systems debugging skills, with experience diagnosing asynchronous workflows, cascading failures, and complex production incidents.
- Ability to code for automation and engineering tooling using Go, Python, or a similar programming language.
- Solid database knowledge across SQL and NoSQL technologies, particularly PostgreSQL, MongoDB, and Redis, including indexing, replication, and performance optimization.
- Proven technical leadership experience, including setting reliability standards, influencing architecture across teams, and mentoring engineers.
- Advanced written and spoken English communication skills.
- Experience with AI or MLOps infrastructure, including model serving, LLM inference, GPU/resource management, or AI agent observability, is highly advantageous.
- Familiarity with multi-tenant container platforms and customer workload infrastructure is a plus.
- Experience with data pipelines and orchestration tools such as Airflow or Prefect, and data platforms such as Databricks, Snowflake, or BigQuery, is beneficial.
- Familiarity with incident management platforms such as PagerDuty, Opsgenie, or incident.io is an advantage.
- Experience in the payments industry is preferred.
- Additional experience with ECS, s6-overlay, AI agent frameworks, or Spanish proficiency is a plus.
Benefits
- Competitive compensation.
- Fully remote working environment with the flexibility to work from different locations.
- One-time home office allowance to help create an effective workspace.
- Company-provided work equipment.
- Stock options.
- Health plan available wherever you are.
- Flexible days off.
- Access to language, professional, and personal development courses.
- Opportunity to work on globally scaled infrastructure supporting complex payment and AI workloads.
- Significant technical ownership and influence over reliability strategy, architecture, and engineering standards.
- Collaborative international environment with opportunities to mentor engineers and shape organization-wide engineering practices.
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!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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