Remote MLOps Engineer: Pipelines & Production AI

Emapta

Metro Manila

Remote

PHP 1,000,000 - 1,800,000

Full time

9 days ago

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Benefits offered by this job

Day 1 HMO coverage with free dependent

Competitive Salary Package

Day Shift schedule

Fixed weekends off

Permanent WFH arrangement

Salary Advance Program

Unlimited upskilling through Emapta

Free 24/7 access to our office gyms

Exclusive Emapta Lifestyle perks

Unlimited opportunities for employee.

Job summary

Emapta is hiring an ML Ops Engineer to build pipelines, monitoring, and feedback loops behind AI for education for an Australian education technology client. The role offers permanent remote work, day shift with Melbourne time, and opportunities to grow with top talent.

Ideal candidates have 3–5 years in software engineering or MLOps, strong Python, and experience with pipelines and cloud services. Benefits include day 1 HMO, competitive salary, and ongoing upskilling through Emapta Academy.

Qualifications

3-5 years of relevant experience in software engineering, MLOps, or LLMOps.
Strong Python engineering skills and experience with relevant ML libraries and workflows.
Experience building data and machine learning pipelines using tools such as Dagster, Airflow, Prefect, or Kubeflow.
Strong understanding of model monitoring, experiment tracking, and CI/CD for machine learning.
Familiarity with AWS or GCP data and compute services.
Experience with Docker and Infrastructure as Code tools such as Pulumi, CloudFormation, or Terraform.
Strong collaboration, communication, adaptability, and problem-solving skills.
A strong sense of ownership and accountability for results.

Responsibilities

Design and implement MLOps infrastructure that enables teams to experiment quickly and release models with confidence
Build tools for model versioning, evaluation, monitoring, and feedback across content generation, recommendations, classification, and assessment insights
Establish structured experimentation processes with reproducible results and clear decision criteria
Automate LLM prompt pipelines and integrate them safely and reliably with production systems
Promote best practices in model lifecycle management, performance, and production monitoring
Mentor peers on MLOps and engineering practices and encourage collaboration across teams