CW | Senior DevOps Engineer

Cloud foundation - cost & risk

• Drive cost efficiency across a multi-account AWS environment: cost dashboards, budgets, right-sizing of compute and databases, log-volume reduction, storage lifecycle policies, and Savings Plan coverage.
• Eliminate platform risk by retiring end-of-life runtimes, simplifying over-engineered infrastructure, and consolidating accounts with consistent guardrails (SCPs) and tagging.
• Bring AI/Bedrock spend under governance with attribution and budgets.

Reliability as a product

• Extend SLO coverage across production services and manage SLOs and monitors as code in Terraform.
• Make error budgets actionable, cut alert noise, measure MTTD/MTTR, and integrate alerting cleanly into the incident-management workflow.
• Establish and run disaster-recovery drills with documented recovery objectives

Self-service developer platform

• Extend our reusable CI/CD workflows and golden-path service templates so new services reach production in hours, not days.
• Build out an internal developer platform (self-service deployments, ephemeral environments) and a production API Gateway for external APIs.
• Move secrets to runtime injection via AWS Secrets Manager, harden the container build pipeline, and consolidate infrastructure-as-code onto a single approach.

AI-augmented operations

• Build and maintain a library of reusable skills for our AI harness (pipeline fixes, incident analysis, cost optimisation, infrastructure generation) and roll out MCP servers that make ops data queryable by engineering teams.
• Apply agentic AI to incident root-cause analysis and IaC authoring, with sensible human in-the-loop guardrails.

Must have:

• 5+ years in DevOps / Platform / SRE / Cloud Engineering, with senior ownership of production systems.
• Deep AWS expertise - ECS/Fargate, RDS/Aurora (MySQL & PostgreSQL), Lambda, IAM, VPC, S3, CloudWatch, API Gateway - and comfort operating a multi-account AWS Organisation.
• Terraform at production scale: modules, workspaces, multi-environment, and code review discipline.
• CI/CD with GitHub Actions - reusable workflows, composite actions, and OIDC-based cloud authentication.
• Containers & Docker - building, securing, and operating containerised workloads.
• Observability - hands-on with Datadog (or Prometheus/Grafana/New Relic equivalents): APM, SLOs, monitors-as-code, and log/metric/trace pipelines.
• Cloud cost management (FinOps) - right-sizing, Savings Plans/RIs, and cost attribution through tagging.
• Reliability practice - SLOs, error budgets, on-call, blameless RCA, and MTTR/MTTD measurement.
• Strong scripting (Python and/or Bash) and the ability to work across a polyglot codebase (PHP, TypeScript, Python).
• Security-conscious by default: least-privilege IAM, secrets management, and WAF/GuardDuty/Security Hub-style controls.
• AI-augmented engineering - fluent with agentic coding harnesses (e.g. Claude Code) and MCP, building reusable skills and prompts to automate operational toil. You treat AI as a core part of your workflow, not an afterthought.
• Strong English communication skills, especially in the global software development environment

Nice to have:

• Built agentic workflows or custom MCP servers/tools that expose ops data (logs, metrics, infra) to AI harnesses.
• Experience building an internal developer platform or self-service tooling.
• API Gateway and external-API productionisation (rate limiting, usage metering).
• Blue-green / canary deployments (we use AWS CodeDeploy).
• Data-platform infrastructure - Databricks, dbt, MWAA/Airflow, EventBridge/SQS, or streaming architectures.
• Jira Service Management / Atlassian incident & problem management.
• Kubernetes (EKS) - including the judgement to know when not to reach for it.
• Significant migration experience (CI platform migrations, major database engine upgrades).

Due to the large number of applicants, only shortlisted candidates will be contacted.