Sr. DevOps Engineer
Senior DevOps Engineer owning Floward's e-commerce and data platform on AWS (with some GCP): leading the move to Terraform-managed, GitOps-driven infrastructure, running EKS, GitHub Actions CI/CD and observability stacks, while using AI tools daily and supporting LLM workloads in production. Onsite in Kuala Lumpur.
- Compensation
- Not disclosed
- Location
- Kuala Lumpur, Malaysia
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Devops
- Posted
Skills
Must have
- 5+ years in DevOps, SRE or cloud infrastructure roles with production systems owned
- Strong hands-on AWS experience: EC2, EKS, S3, RDS, IAM, VPC, CloudWatch
- Deep practical Terraform experience: modules, remote state, refactoring infrastructure into code
- Production Kubernetes experience, ideally EKS: upgrades, autoscaling, networking, troubleshooting
- Strong CI/CD experience with GitHub Actions or similar
- Working knowledge of GitOps with Flux CD or Argo CD, and of Helm
- Experience operating and troubleshooting PostgreSQL and DocumentDB (or MongoDB)
- Scripting in Python, Bash or Go for automation
- Daily, effective use of AI tools in engineering work, with showable examples
- Clear grasp of AI limits and risks: validating output, keeping secrets out of prompts, prompt injection and over-privileged agents
- Clear communication in English and a collaborative approach
- Ability to own work end to end with minimal supervision
Nice to have
- GCP experience or multi-cloud design experience
- Built LLM-based tools or agents (Claude, OpenAI or Bedrock APIs, LangChain, MCP)
- Running AI/ML workloads on Kubernetes: GPU node pools, vLLM, KServe, vector databases
- AIOps experience: anomaly detection, alert correlation, automated remediation
- Security tooling in pipelines (Trivy, Checkov, tfsec, OPA) and secrets tools (Vault, AWS Secrets Manager, External Secrets)
- FinOps practices and cloud cost optimisation
- Experience in e-commerce or high-traffic distributed systems, including peak-season readiness
Full description
We're hiring a Senior DevOps Engineer to help own and evolve the platform behind our large-scale e-commerce and data products. Our infrastructure runs mainly on AWS, with a smaller GCP footprint. We're moving to a fully Terraform-managed, GitOps-driven and highly automated platform, and we expect AI to be a core part of how we build and run it. You'll work with DevOps engineers, software developers and data teams to deliver reliable environments, fast and safe releases, and strong observability. You'll use AI tools daily to move faster, and help the team adopt them safely. This role suits a hands-on engineer who unblocks themselves, takes ownership end to end, and raises the bar for the people around them.
Job Responsibilities :
Cloud and infrastructure as code:
- Design, build and operate infrastructure on AWS, and on GCP where needed.
- Lead parts of our migration to Terraform: write reusable modules, set standards, and bring unmanaged resources under code.
- Own the health, scaling, upgrades and cost efficiency of our EKS clusters.
- Keep dev, staging and production consistent and reproducible.
CI/CD and GitOps:
- Build and maintain GitHub Actions pipelines with automated build, test, security scanning and deployment.
- Run and improve Flux CD GitOps flows, Helm charts and application manifests.
- Diagnose failed builds and deployments with engineering teams, then fix the root cause.
AI-enabled engineering and automation:
- Use AI coding assistants and agents to write and review Terraform, Kubernetes manifests, pipelines and scripts, with sound validation and review habits.
- Build AI-powered automation for the team, such as incident triage, log and alert summarisation, PR review helpers, or ChatOps bots.
- Connect AI agents to our tooling safely (e.g., via MCP servers or APIs), with least-privilege access, approval gates and audit trails.
- Support AI and LLM workloads in production: model APIs (Amazon Bedrock, Vertex AI, OpenAI), LLM gateways, and their cost, latency and observability.
- Share prompts, workflows and practices so the whole team gets faster with AI.
Observability, reliability and security:
- Run and extend our observability stack (Prometheus, Grafana, Loki, CloudWatch), with clear dashboards, SLOs and actionable alerts.
- Take part in on-call and incident response, and lead blameless post-incident reviews.
- Apply strong practices for IAM, secrets management, network security and compliance, including for AI tools that touch code or infrastructure.
- Look for cost savings across AWS and GCP and act on them.
Collaboration and continuous improvement
- Partner with developers, QA and data teams to improve delivery speed and stability.