Production AI/ML Engineer - GenAI & MLOps

GECO Asia Pte. Ltd; Santo Niño 1st; On-site; PHP 1,000,000 - 2,000,000; Full time

Job summary

GECO Asia Pte. Ltd seeks a Senior AI/ML Engineer (MLOps) to design, deploy, monitor, and improve large-scale AI/ML solutions in production.

You will ensure reliability, governance, and operational excellence across GenAI and traditional ML systems, collaborating with data scientists, engineers, and business stakeholders. The role emphasizes model lifecycle management, monitoring, and on-call readiness, with a focus on scalable, cost-efficient deployments and strong communication to both

Qualifications

• Bachelor's degree in CS/Engineering or related field.
• 6+ years in ML/AI, MLOps or related software roles.
• Experience deploying production AI/ML in cloud environments.
• Strong Python, SQL, and model maintenance skills.
• Experience with monitoring, governance, and on-call support.

Responsibilities

• Design monitoring, alerting, and observability for ML/AI systems.
• Build automated evaluation and validation pipelines for models and GenAI workflows.
• Resolve production issues related to model behavior and data quality.
• Manage model, prompt, embedding, and vector index lifecycles.
• Collaborate with platform teams to optimize deployments for scale and cost.
• Establish feedback loops from metrics and user interactions to improve systems.
• Ensure governance, security, and privacy best practices.
• Create incident reports and performance benchmarks.
• Communicate insights and health metrics to stakeholders.
• Mentor junior engineers through code reviews.

Skills

Python

SQL

Machine Learning

MLOps

Cloud (Azure)

On-call support

Prometheus

Grafana

Education

Bachelor's degree

Tools

Azure ML

Databricks

LlamaIndex

CrewAI

Job description

GECO Asia Pte. Ltd seeks a Senior AI/ML Engineer (MLOps) to design, deploy, monitor, and improve large-scale AI/ML solutions in production.

You will ensure reliability, governance, and operational excellence across GenAI and traditional ML systems, collaborating with data scientists, engineers, and business stakeholders. The role emphasizes model lifecycle management, monitoring, and on-call readiness, with a focus on scalable, cost-efficient deployments and strong communication to both