Forward Deployed Engineer - MLOps

Systems Limited; Kuala Lumpur; On-site; MYR 180,000 - 240,000; Full time

Job summary

Systems Limited is seeking an experienced MLOps-focused professional to own the production lifecycle of ML models, ensuring validated models are reliably deployed, monitored, and scaled. The role emphasizes CI/CD, cloud infrastructure, automation, observability, cost optimization, and incident management.

You will lead pipelines, collaborate with Data Scientists, mentor engineers, and engage with presales to prove production readiness. On-call duties may apply.

Qualifications

• 6+ years in MLOps/ML Platform Eng with production ownership.
• Strong CI/CD, containerization, cloud infra and ML observability.
• IaC and automated deployment pipelines experience.
• Experience on AWS/Azure/GCP and FinOps practices.
• Ability to communicate incidents to business stakeholders, clearly.

Responsibilities

• Own production serving, CI/CD, deployment and monitoring of ML models.
• Build and maintain model retraining, versioning and deployment pipelines.
• Manage ML infrastructure and optimize cloud costs via FinOps.
• Implement observability, alerting, drift detection and performance monitoring.
• Own production incident response and on-call support.
• Collaborate with Data Scientists to design scalable, production-ready systems.
• Support presales/PoCs by demonstrating production readiness and scalability.
• Mentor engineers on MLOps and production-readiness best practices.
• Communicate infrastructure cost, performance and reliability trade-offs to non-technical stakeholders.

Skills

MLOps

CI/CD

Cloud infrastructure

Observability

FinOps

Model lifecycle

IaC

Mentoring

Stakeholder communication

Tools

AWS

Azure

GCP

Docker

Kubernetes

Terraform

Monitoring tools

Job description

Own the production lifecycle of machine learning models, ensuring validated models are reliably deployed, monitored, optimized, and maintained at scale. The role focuses on MLOps, cloud infrastructure, automation, observability, cost optimization, and production incident management.

Responsibilities:

• Own production serving, CI/CD, deployment, and monitoring of ML models.
• Build and maintain model retraining, versioning, and deployment pipelines.
• Manage ML infrastructure and optimize cloud costs through FinOps practices.
• Implement observability, alerting, model drift detection, and performance monitoring.
• Own production incident response, troubleshooting, and on-call responsibilities.
• Collaborate with Data Scientists and ML Engineers to design scalable, production-ready systems.
• Support presales and PoCs by demonstrating production readiness and scalability.
• Mentor engineers on MLOps and production-readiness best practices.
• Communicate infrastructure cost, performance, and reliability trade-offs to non-technical stakeholders.

Qualifications:

• 6+ years of experience in MLOps, ML Platform Engineering, or related roles with proven production ownership.
• Strong expertise in CI/CD, containerization, cloud infrastructure, and ML observability.
• Deep understanding of the ML model lifecycle, including retraining, model versioning, and drift detection.
• Experience with Infrastructure as Code (IaC) and automated deployment pipelines.
• Strong knowledge of major cloud platforms such as AWS, Azure, or GCP.
• Experience with cloud cost monitoring, optimization, and FinOps practices.
• Strong understanding of monitoring, alerting, SLA management, and production incident response.
• Ability to troubleshoot and communicate technical incidents clearly to business stakeholders.
• Strong collaboration and mentoring skills.
• Willingness to participate in on-call and off-hours production support