LLM & AI Systems Engineer for Customer Support

Maya; Mandaluyong; On-site; PHP 1,200,000 - 2,100,000; Full time

Job summary

Maya in Manila is seeking a Senior Machine Learning Engineer to build and operate ML/LLM-based solutions that power customer experience channels. You will work across data science, engineering, and platform teams to deliver scalable AI services with measurable impact.

You will deploy ML models as production-ready APIs, monitor performance, optimize costs, and communicate with both technical and non-technical stakeholders to drive data-driven decisions.

Qualifications

• Advanced degree in CS, statistics or related field.
• 6+ years in ML/AI or data science with customer-facing work.
• Core ML, deep learning, NLP, LLM applications and deployment.
• Experience deploying ML models as production-ready APIs (FastAPI, Flask).
• Python proficiency and familiarity with ML libraries (TensorFlow, PyTorch).
• Git, GitHub/GitLab, CI/CD basics for pipelines.

Responsibilities

• Develop, deploy, and maintain LLM-based applications for Maya's services.
• Collaborate with data science and engineering to operationalize AI solutions.
• Identify inefficiencies in internal cloud and ML services and drive cross‑functional initiatives.
• Design ML infrastructure that scales while optimizing performance and cost.
• Analyze monitoring, logging, and performance metrics to inform improvements.
• Measure solution impact and iterate based on customer satisfaction and usage.
• Promote data-driven decisions with clear communication to technical and non‑technical peers.

Skills

Core ML

NLP

LLM

Python

MLOps

Git

Education

Master's degree or Bachelor's in CS/Stats

Tools

FastAPI

Flask

AWS

Databricks

GCP

Azure

Snowflake

Boto3

Pydantic

Job description

Maya in Manila is seeking a Senior Machine Learning Engineer to build and operate ML/LLM-based solutions that power customer experience channels. You will work across data science, engineering, and platform teams to deliver scalable AI services with measurable impact.

You will deploy ML models as production-ready APIs, monitor performance, optimize costs, and communicate with both technical and non-technical stakeholders to drive data-driven decisions.