MLOps Engineering Manager: Scale ML Platform

FinTech Collective, LLC; Kuala Lumpur; On-site; MYR 180,000 - 260,000; Full time

Job summary

Gen in Kuala Lumpur is seeking a Manager for MLOps Engineering to lead the development of MoneyLion's machine learning platform and infrastructure in a fast-paced environment. You will guide cross-functional teams to deploy ML at scale and establish best practices with AI Devops and data teams.

You will collaborate with Data Scientists, Data Engineers and IT/DevOps to drive automation, governance and cost efficiency, while maintaining on-call readiness for critical incidents.

Qualifications

• Must lead and mentor MLOps engineers in research, design and prototyping.
• Experience deploying ML at scale and managing data pipelines.
• Proficient with Kubernetes (preferably EKS) and production environments.
• Strong Python, Java and SQL skills.
• Ability to optimize cloud infrastructure costs and ensure governance.

Responsibilities

• Lead a team of MLOps engineers to prototype and adopt MLOps practices.
• Collaborate with Data Scientists, Data Engineers, and AI/ML teams to improve workflows.
• Increase automation in ML development and deployment processes.
• Coordinate with IT operations and DevOps to integrate platforms.
• Set up monitoring and governance for ML models in production.
• Oversee cost transparency and optimization for cloud usage.
• Develop disaster recovery and business continuity plans for cloud platforms.
• Participate in on-call rotation for incident response.

Skills

Team leadership

Python

SQL

Java

Kubernetes

Cloud cost

Troubleshooting

Communication

Data analysis

Software fundamentals

Tools

DVC

MLFlow

Metaflow

Ray

Chalk

Job description

Gen in Kuala Lumpur is seeking a Manager for MLOps Engineering to lead the development of MoneyLion's machine learning platform and infrastructure in a fast-paced environment. You will guide cross-functional teams to deploy ML at scale and establish best practices with AI Devops and data teams.

You will collaborate with Data Scientists, Data Engineers and IT/DevOps to drive automation, governance and cost efficiency, while maintaining on-call readiness for critical incidents.