Forward Deployed Engineer - MLOps

Systems Limited; Islamabad; On-site; PKR 2,400,000 - 4,200,000; Full time

Job summary

Systems Limited is seeking an experienced MLOps/ML Platform Engineer to own end-to-end production of ML models, including deployment, monitoring, and optimization at scale in cloud environments.

You will lead CI/CD pipelines, manage IaC, and collaborate with data scientists to build scalable, production-ready systems while driving cost efficiency and incident response through FinOps practices.

Qualifications

• 6+ years in MLOps or ML Platform Engineering with production ownership.
• Strong CI/CD, containerization, cloud infra and ML observability expertise.
• Deep understanding of ML lifecycle: retraining, versioning, drift detection.
• IaC and automated deployment pipelines experience.
• Cloud platforms AWS/Azure/GCP knowledge.
• Cloud cost monitoring, optimization and FinOps practices.
• Monitoring, alerting, SLA management, and incident response expertise.
• Ability to troubleshoot and communicate incidents to stakeholders.
• Strong collaboration and mentoring skills.

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, drift detection, and performance monitoring.
• Own production incident response, troubleshooting, and on-call responsibilities.
• Collaborate with Data Scientists and ML Engineers to design scalable systems.
• Support presales and PoCs by demonstrating production readiness.
• Mentor engineers on MLOps and production-readiness best practices.
• Communicate infra cost, performance, and reliability trade-offs to stakeholders.

Skills

CI/CD

Containerization

Cloud infrastructure

ML observability

Model lifecycle management

Infrastructure as Code

Monitoring & alerting

FinOps / cost optimization

On-call readiness

Mentoring & collaboration

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.