Senior Site Reliability Engineer

📋 Job Overview

At CloudFactory, we are a mission-driven team passionate about unlocking the potential of AI to transform the world. By combining advanced technology with a global network of talented people, we make unusable data usable, driving real-world impact at scale. We’re a global community founded on strong relationships and the belief that meaningful work transforms lives. Our commitment to earning, learning, and serving fuels everything we do as we strive to connect one million people to meaningful work and build leaders worth following.

🏢 About CloudFactory

At CloudFactory, we believe in building a workplace where everyone feels empowered, valued, and inspired to bring their authentic selves to work. We are mission-driven, people-centric, innovative, and globally connected. If you’re passionate about innovation, collaboration, and making a real impact, we’d love to have you on board!

🎯 The Role

As a Site Reliability Engineer, you will play a key role in keeping all production systems running smoothly. You will work closely with other engineers and operators to fuse engineering principles, operational knowledge, security, and automation to work towards platform/service production excellence from an angle of infrastructure, reliability, and security. The SRE team owns the foundation of AI Platform’s Core platform - the services and infrastructure that let us deploy to a multitude of public cloud providers and that powers many ML and LLM powered features. We give every other engineering team a reliable base to build on, and we own the software delivery lifecycle end to end: the tooling, patterns, and automation that reduce friction for the whole org.

✅ Key Responsibilities

• 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.

📌 Required Qualifications

• 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.
• 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.

⭐ Desirable Experience

• ML & AI platform experience
• Running ML workloads on Kubernetes - GPU scheduling, capacity, and cost management
• Model serving and inference at production scale (e.g., 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

🎁 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 to meaningful work, and be part of a global community, CloudFactory is the place for you.

🛂 Visa & Eligibility

Global Collaboration: Ability to work across global teams and different cultures across various time zones with strong communication skills.