AI Platform Engineer

Job summary

ClearRoute is seeking a Platform Engineer to design, build, and operate modern developer platforms across various industries. The role involves leading technical workstreams, managing CI/CD pipelines, and integrating AI/ML workloads into existing systems.

Qualifications

• Solid hands-on experience with at least one major cloud provider (AWS preferred; GCP or Azure accepted).
• Production experience with Kubernetes and familiarity with the surrounding ecosystem (Helm, ArgoCD / Flux, Karpenter, Cilium, etc.).
• Strong infrastructure-as-code skills, Terraform is the baseline; other tools are a bonus.
• Practical experience designing or operating CI/CD systems (GitHub Actions, GitLab CI, Tekton, or similar).
• Comfort working in Linux environments and writing automation in Python, Bash, or Go.
• The ability to explain complex technical concepts clearly to a mixed audience.
• A consulting mindset: you care about solving the client's actual problem, not just delivering a deliverable.

Responsibilities

• Design and deliver internal developer platforms (IDPs) that improve developer experience and accelerate software delivery.
• Build and maintain infrastructure-as-code using Terraform, Pulumi, or CDK and enforce code review and testing standards.
• Manage and optimise Kubernetes clusters (EKS, GKE, AKS) including multi-tenancy, networking, RBAC, and cost controls.
• Own CI/CD pipelines end-to-end: from source control policies through build, test, security scanning, artefact management, and deployment.
• Implement secrets management and certificate lifecycle automation using HashiCorp Vault or equivalent.
• Embed SRE practices: SLOs, error budgets, runbooks, on-call design, and blameless post-mortems.
• Integrate security tooling (SAST, DAST, dependency scanning, policy-as-code) into delivery pipelines.
• Design and test disaster-recovery strategies; automate them where possible.
• Ensure compliance with client security standards and relevant regulatory frameworks.
• Design and operate infrastructure for AI/ML workloads: GPU node pools, model-serving runtimes (Triton, vLLM, BentoML), and vector database deployments (pgvector, Weaviate, Qdrant).
• Build and maintain MLOps pipelines model training, versioning, evaluation, and promotion to production using platforms such as Kubeflow, MLflow, or cloud-native equivalents.
• Integrate LLM APIs and AI agent frameworks into existing developer platforms, including prompt management, observability, cost controls, and rate-limit guardrails.
• Advise clients on AI readiness: data infrastructure, governance, security controls (model access policies, output filtering), and the organisational changes that sit alongside the technical work.
• Stay current with the fast-moving AI tooling landscape and bring relevant ideas back to the team and to clients.
• Lead technical discovery sessions and platform assessments with client engineering and architecture teams.
• Translate client requirements into clear technical plans and communicate trade-offs to both technical and non-technical stakeholders.
• Coach and upskill client platform and application engineers through pairing, workshops, and code review.
• Produce high-quality documentation, architecture decision records (ADRs), and runbooks that clients can own after the engagement.

Skills

Relocation