DevOps Engineer

We are seeking a DevOps Engineer with strong experience in Azure, Kubernetes, Terraform, CI/CD, and LLMOps to design and operate AI-focused delivery platforms. The role focuses on infrastructure, Kubernetes, Terraform, CI/CD, LLMOps, security, observability, reliability, and production deployment, while driving platform engineering standards and supporting multiple engineering teams.

Key Responsibilities:

• Design, provision, and operate infrastructure supporting AI application workloads across development, staging, and production environments.
  
• Own Infrastructure-as-Code definitions using Terraform, including module design, state management, environment segregation, and drift detection.
  
• Design and operate Kubernetes workloads, including namespace strategy, resource governance, autoscaling, and network policy enforcement.
  
• Define environment topology, environment promotion strategy, and configuration management across environments.
  
• Design, implement, and maintain CI/CD, including reusable workflow libraries and shared templates.
  
• Define branching, versioning, tagging, and release management strategy across multiple repositories and teams.
  
• Implement automated quality gates covering linting, unit and integration testing, static analysis, vulnerability scanning, and policy checks.
  
• Implement progressive delivery patterns, including blue/green and canary deployments, feature-flagged rollout, and automated rollback.
  
• Own release readiness verification, including rollback rehearsal and post-rollback regression validation for both new and existing user paths.
  
• Reduce build and deployment cycle time through caching, parallelisation, and pipeline instrumentation.
  
• Build and operate deployment pipelines for LLM-integrated applications, RAG services, and vector store dependencies.
  
• Automate AI evaluation pipelines (RAGAs, Langfuse, Phoenix) as pre-deployment gates, with defined thresholds for retrieval quality and response consistency.
  
• Integrate guardrail and grader checks into CI/CD so that Responsible AI validation is enforced automatically rather than manually.
  
• Own secrets and credential management, including rotation policy and least-privilege access.
  
• Implement identity and access controls.
  
• Integrate static code analysis and open-source vulnerability scanning into pipelines, including agentic AI scanning tooling used on the program.
  
• Maintain audit-ready evidence of deployments, approvals, and access changes to satisfy client governance requirements.
  
• Implement and maintain observability standards across logging, metrics, tracing, and alerting.
  
• Own backup, restore, and disaster recovery design, including periodic restore validation.
  
• Define and enforce platform engineering standards, deployment checklists, and change management discipline across teams.
  
• Produce and maintain architecture diagrams, runbooks, and operational documentation.
  
• Support client-facing technical discussions on release governance, security posture, and operational readiness.
  

Requirements

What are we looking for:

• Minimum of 5 years of hands-on DevOps, Platform Engineering, or Site Reliability Engineering experience.
  
• Experience supporting multiple engineering teams from a shared platform or centre-of-excellence model.
  
• Exposure to AI-enabled, data-intensive, or high-throughput API workloads is strongly preferred.
  
• Strong hands-on depth in Azure Kubernetes Service (AKS), including upgrade strategy, node pool design, and workload isolation.
  
• Working expertise across Azure Container Registry, Azure Key Vault, Azure Monitor and Log Analytics, Application Gateway / Front Door, Azure Storage, and Azure networking.
  
• Strong understanding of Microsoft Entra ID, Azure RBAC, managed identities, and service principal governance.
  
• Strong hands-on experience with GitHub Actions, including reusable workflows, composite actions, self-hosted runners, and environment protection rules.
  
• Strong hands-on experience with Azure DevOps, including Pipelines (YAML), Repos, Artifacts, and Environments with approval gates.
  
• Experience with artifact management, versioned releases, and dependency promotion across environments.
  
• Strong hands-on expertise in Terraform, including module authoring, remote state, workspaces, and policy-as-code.
  
• Strong scripting capability in Python and Bash for automation and tooling.
  
• Strong working knowledge of container fundamentals, image optimisation, and multi-stage builds.
  
• Hands-on experience with Docker-based workflows.
  
• Strong Kubernetes operational capability, including deployments, services, ingress, secrets, config maps, probes, RBAC, HPA, and resource quotas.
  
• Understanding of LLM application architectures, RAG pipelines, and vector database deployment and operational characteristics.
  
• Experience integrating AI evaluation frameworks (RAGAs, Langfuse, Phoenix) into automated pipelines.
  
• Understanding of prompt versioning, prompt registry patterns, and configuration-driven prompt deployment.
  
• Understanding of guardrail enforcement and Responsible AI validation within a delivery pipeline.
  
• Strong understanding of IAM, OAuth2 / OIDC, and secure API design.
  
• Experience with secrets management, credential rotation, and least-privilege enforcement.
  
• Experience integrating SAST, dependency, container, and IaC scanning into pipelines.
  
• Hands-on experience with Prometheus, Grafana, and OpenTelemetry.
  
• Strong incident management capability, including structured root cause analysis and preventive action tracking.
  
• Familiarity with data pipeline deployment and scheduled workload orchestration.
  
• Understanding of application architecture patterns, including microservices, REST APIs, asynchronous processing, and streaming, sufficient to troubleshoot across the stack.
  
• Strong ownership and accountability for platform stability and delivery outcomes.
  
• Ability to communicate technical trade-offs, risk, and operational impact to both engineering and business stakeholders.
  
• Strong collaboration across multiple engineering pods, with the maturity to standardise rather than customise per team.
  
• Reliability and safety mindset, with a bias towards reversible, verifiable change.
  
• Openness to feedback and continuous improvement.
  

Language Requirements:

• English: Working proficiency required.
  
• Japanese: Working proficiency required.