Machine Learning Operation Analyst (MLOps)

TL;DR

Machine Learning Operation Analyst (MLOps): Monitoring and optimizing ML models and automating data pipelines to bridge the gap between data science and production with an accent on scalability and system reliability. Focus on building monitoring systems, simplifying model serving workflows, and promoting MLOps excellence across global teams.

Location: Hybrid in Kuala Lumpur, Malaysia

Company

A global consumer intelligence company that leverages AI and Machine Learning to provide comprehensive insights into consumer buying behavior and market trends.

What you will do

• Build and maintain systems to track ML model performance and output quality.
• Simplify and automate workflows around data pipelines and model serving.
• Collaborate within global cross-functional teams of data scientists, engineers, and product managers.
• Deep-dive into ML systems to troubleshoot issues and implement long-term technical solutions.
• Promote code quality, monitoring standards, and MLOps best practices across the organization.
• Contribute to internal Communities of Practice and engage in mentorship and training.

Requirements

• 4+ years of relevant work experience.
• Strong skills in SQL and Python.
• Experience with monitoring tools such as Kibana, Grafana, OpenSearch, or Looker.
• Familiarity with version control tools (GitLab, GitHub).
• Solid understanding of the ML lifecycle and model performance metrics.
• Undergraduate (BSc.) degree in Computer Science, Mathematics, or a related field.
• Professional working proficiency in English.
• Must be based in Kuala Lumpur.

Nice to have

• Experience with cloud platforms (AWS, GCP, Azure).
• Knowledge of containerization and orchestration (Docker, Kubernetes).
• Exposure to workflow orchestration tools like Airflow or Kubeflow.
• Prior experience in market research, pricing, or revenue assurance.

Culture & Benefits

• Flexible working environment with hybrid work options.
• Access to LinkedIn Learning and ongoing professional development training.
• Employee Assistance Program (EAP) and volunteer time off.
• Opportunities for personal and professional growth within a global tech team.