About the job Machine Learning Engineer
Beepo is a Probe Group company, a full-service Australian-owned outsourcing company. With 12,600 people and over 15,000 deployed workstations across 6 countries.The largest and most diverse Australian-owned customer experience outsourcing provider.
Our Beepo office is located in Clark, Pampanga. We provide exceptional workforce support to clients all over the world. Our primary focus is to help businesses grow sustainably with the help of fantastic Philippine talent.
You will be supported by a great working environment, leading benefits, and an opportunity to meet your career goals. You will be able to apply your skills and innovative thinking to this role. This is an opportunity to grow your career quickly
JOB DESCRIPTION
Machine Learning Engineer
(MLOps Engineer/Machine Learning Developer)
Duties and Responsibilities:
Individual contributor role focused on building the machine learning foundations that support Edrolo's intelligent product features (content generation, recommendations, classification, and assessment insights). Day-to-day:
- Design and implement MLOps infrastructure so teams can experiment fast and ship with confidence.
Build tooling for model versioning, evaluation, monitoring and feedback loops.
- Introduce "think like a scientist" processes — structured experimentation, reproducibility, clear decision frameworks.
- Automate LLM prompt pipelines and integrate them safely and reliably into production systems.
- Drive best practices for model lifecycle management, performance, and monitoring in production.
- Mentor peers in MLOps and engineering best practices; advocate for cross-functional collaboration.
Qualifications
- Strong Python engineering skills, with experience developing production-ready applications, services, or ML solutions.
- Broad experience in Machine Learning Engineering, including developing, deploying, and maintaining machine learning solutions.
- Hands-on experience building data and ML pipelines using modern workflow or orchestration frameworks.
- Good understanding of software engineering best practices, including version control, testing, and code quality.
- Experience working with ML workflows from development through deployment and production support.
- Experience with MLOps or LLMOps libraries, tools, and workflows.
- Familiarity with model monitoring, experiment tracking, and ML-focused CI/CD practices.
- Experience working with AWS or GCP data and compute services.
- Experience with containerisation technologies such as Docker.
- Familiarity with Infrastructure as Code (IaC) tools and practices.
- Exposure to deploying and maintaining machine learning models in production environments.