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# Senior Site Reliability Engineer

- **Company:** CloudFactory Limited
- **Location:** Reading, UK
- **Experience:** Expert
- **Salary:** £76,140.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, DevOps, Programming Tools, Github, Python (Programming Language), Machine Learning, Open Source Technology, Reliability Engineering, Data Logging, Autoscaling, Istio, Large Language Models, Grafana, Mttr, Backend, Cloudformation, AI Platforms, Kubernetes, Performance Monitor, Machine Learning Operations, Front End Software Development, Terraform, Software Version Control
- **Published:** September 2, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5865192387

## About the Role

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.

## Description

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.

This is an exciting opportunity to grow professionally while contributing to a mission-driven organization.

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.


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