DevOps Engineer

Digile Pte Ltd; Kampung Malaysia Tambahan; On-site; MYR 120,000 - 180,000; Full time

Benefits offered by this job

Travel to customer sites

Exposure to AI/ML Ops

Job summary

Digile Pte Ltd’s AI Practice seeks a DevOps Engineer with an MLOps focus to join our shared engineering pool serving customer-embedded AI delivery pods in Kuala Lumpur, Malaysia.

You will own deployment, infrastructure and reliability for AI workflows, working with Senior and Junior FDEs and AI Product Managers to ensure workflows are deployable, monitorable and governable across multiple pods.

Qualifications

• 3-5 years of experience in DevOps, SRE, platform or cloud engineering with MLOps exposure.

Responsibilities

• Design, build and maintain CI/CD pipelines for AI workflows.
• Set up and manage cloud infrastructure with Infrastructure as Code.
• Implement monitoring, logging and alerting including LLM observability.
• Maintain data, model and output quality drift targets and uptime.
• Own deployment and reliability including incident response.

Skills

CI/CD pipelines

SRE mindset

Security & compliance

Bash & Python scripting

Communication skills

DevOps planning

Tools

Azure DevOps

GitHub Actions

Docker

Kubernetes

Terraform

CI/CD tooling

Job description

Digile’s AI Practice is currently looking for a DevOps Engineer with MLOps focus to join the shared engineering pool supporting our Forward Deployed Engineer (FDE) Pods, customer-embedded, cross-functional AI delivery units that turn AI capabilities into production-grade workflows with measurable business impact. The ideal candidate brings a blend of strong skillsets as requested in the section below and a strong passion for running reliable, secure systems in production.

The main job responsibility of the DevOps Engineer is to own deployment, infrastructure and reliability for AI workflows. As the DevOps Engineer, you hold decision rights on deployment and infrastructure and will work closely with Senior FDEs, Junior FDEs and AI Product Managers across multiple pods to ensure every workflow is deployable, monitorable and governable. You must be capable of working independently, thinking strategically and having fun along the way!

• Designing, building and maintaining CI/CD pipelines for AI workflows, including versioning of prompts and configurations and automated evaluation gates.
• Setting up and managing cloud infrastructure and environments (development, staging and production) using Infrastructure as Code.
• Implementing monitoring, logging and alerting, including LLM observability such as tracing, latency, token cost and error rates.
• Implementing drift detection for data, model and output quality, and maintaining stability targets for uptime, drift and error rates.
• Owning deployment and reliability, including incident response, root cause analysis and continuous improvement.
• Supporting production rollout with the Senior FDE and supporting the infrastructure for data pipelines (ETL, vector databases).
• Ensuring security and governance compliance, including secrets management, identity and access management, network security, audit logging and data protection requirements such as Malaysia’s PDPA.
• Building reusable deployment templates and standards that can be shared across pods.
• Contributing to monthly architecture reviews and to weekly delivery reports.
• Maintaining technical competency and remaining current in cloud, DevOps and MLOps technology and changes in the industry.
• Willing to travel and be stationed at customer sites, within and outside of Kuala Lumpur, Malaysia.

Experience

• 3-5 years of experience in DevOps, Site Reliability Engineering, platform or cloud engineering, with exposure to MLOps or LLMOps.
• Experience deploying and operating production applications on Microsoft Azure, AWS or Google Cloud.
• Experience running containerised workloads with Docker and Kubernetes.
• Experience implementing security and compliance controls in enterprise or regulated environments.
• Relevant cloud or Kubernetes certifications will be a plus point.

Skills

• Hands-on experience with CI/CD tools such as GitHub Actions, GitLab CI, Azure DevOps or Jenkins, including release and promotion controls for moving AI capabilities from POC to production.
• Infrastructure as Code using Terraform, Bicep or CloudFormation.
• Observability tooling such as Prometheus, Grafana, ELK or Azure Monitor; LLM observability tools such as Langfuse, LangSmith or MLflow will be a plus point.
• Experience operating integration infrastructure such as API gateways, scheduled data sync/ETL jobs and webhooks for SaaS platforms, e.g. Autodesk Construction Cloud (ACC) Autodesk Forma, CxAlloy and read-only Primavera P6 data feeds.
• Good knowledge of identity and access management, including SSO (SAML/OIDC), Microsoft Entra ID / Active Directory and role-based access control (RBAC), as well as secrets management, TLS and network security.
• Strong understanding of data and model governance, including audit logging, data lineage, data residency and configuring AI services so client data is isolated and not used for model training.
• Familiarity with enterprise security frameworks and client security reviews, e.g. ISO 27001 or NIST, will be a plus point.
• Good scripting skills in Bash and Python.
• Understanding of database and data platform operations, including relational and vector databases and data sources for reporting dashboards.
• Strong ability to work with client IT and cybersecurity teams and to present information in a manner that is clear, concise and open.
• Excellent written and verbal communication skills.