Senior Site Reliability Engineer

Responsibilities:

What you’ll own Reliability of platform(includes ML and LLM workloads) - model serving and inference infrastructure (GPU-backed endpoints, autoscaling, latency and cost tradeoffs), with SLOs, on-call, and incident response that cover models, not just services Observability(includes ML models) - drift and performance monitoring for ML, plus LLM-specific tracing, evals, and guardrails, wired into the same metrics and logging stacks we run everywhere else Company-wide technical direction: shaping the roadmap and building golden paths that raise the baseline for every team Developer tooling and automation that compounds - reusable GitHub Actions, GitOps workflows, Terraform modules - so every engineer ships faster Reusable components packaging common open-source tools (Grafana, Istio, CloudNative stack, and ML tooling such as model registries and feature stores) for teams to deploy in any environment Secure-by-default infrastructure - baking security, compliance audits, cost governance, and audit trails into the platform in close partnership with our lead/backend/staff engineers. Requirements Who you are (must-haves) 5+ years in infrastructure engineering, DevOps, or SRE, operating large-scale, high-availability production systems using Kubernetes Production Operational experience - a live cluster under real load, not a lab. Fluent with Helm, and Terraform or Cloudformation, on at least one major cloud (AWS preferred). Good proficiency in Python or Go or general scripting for automation and tooling(automation with higher language preferred) AI is already in your daily loop - Agentic tooling (Claude Code, Codex, Droid, internal skills) is part of how you ship and not what you are experimenting with. We believe AI tools can be great with human judgement and we want the SRE team to bring the next wave day to day operations. First-principles reasoning - Reasoning from constraints and failure modes naming the tradeoff in business terms (reliability vs. velocity, cost vs. blast radius, standardisation vs. one-off) At least one infrastructure build you owned end to end - with the outcome metric attached (deploy time, MTTR, cost, adoption, availability). Cross-functional strength. Track record working with product, backend/frontend teams to pull through collective initiative. ML & AI platform (strongly preferred) Running ML workloads on Kubernetes - GPU scheduling, capacity, and cost management Model serving and inference at production scale (eg KServe, RayServe, Triton, vLLM, or similar) with real latency and cost constraints(preferred RayServe) MLOps pipeline tooling - training pipelines, model registries, feature stores, and lineage (Kubeflow, MLflow, Feast, Weights & Biases, or equivalents) LLMOps in production - inference serving, prompt/version management, and LLM observability (tracing, evals, drift, guardrails, cost per request) Governing ML/LLM workloads as platform capabilities: data-residency and PII controls, and audit trails Any other General requirements Global Collaboration: Ability to work across global teams and different cultures across various time zones with strong communication skills. Problem Solving: Ability to break down complex problems into simple, actionable solutions. Ownership & Drive: Tendency to go above and beyond to meet deadlines, manage own deliverables, and assist team members. Availability: Willingness to support processes for 24x7 operational support. Benefits At CloudFactory, we believe that work should be more than just a job—it should be a platform for growth, impact, and community. Here, you’ll earn with purpose, learn every day, and serve a mission that truly matters. If you're looking for a career where you can develop professionally, contribute meaningfully, and be part of a global movement, we’d love to have you on this journey! Join us today and be part of our mission to connect people and technology for a better world! Apply now and bring your whole, authentic self to work—we can’t wait to meet you! Find Jobs in Germany on Arbeitnow

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Source: Statistisches Bundesamt (Destatis), table; dl-de/by-2-0; StarIn's calculation