MLOps Engineer

2 weeks ago

Taguig, National Capital Region, Philippines YONDU INC. Full time

Job Description

  • Execute performance tuning activities for model serving infrastructure to maintain optimal latency and throughput.
  • Conduct post-deployment validation checks to ensure model prediction stability, API responsiveness, and overall service quality.
  • Support the enhancement of operational pipelines, including CI/CD workflows, configuration templates, and automated monitoring scripts.
  • Participate in service reliability reviews to improve platform uptime, incident response processes, and operational readiness.
  • Coordinate closely with DevOps and Platform Engineering to address infrastructure-level concerns related to model hosting and deployment.
  • Assist in the rollout of platform-level improvements, including model registry enhancements, container optimization, and new monitoring tools.

Job Qualifications

  • Related Work Experience: Minimum of 2+ years hands-on experience in a production environment covering MLOps, Data Engineering, or Software Engineering.

  • Demonstrated ability to meet and exceed strict Service Level Agreements (SLAs), especially those related to system uptime, stability, incident response, and resolution.

  • Experience supporting cloud-hosted ML systems in distributed, high-availability environments.

  • Knowledge:

  • Strong understanding of model deployment workflows, including model versioning, serving, rollout strategies, and post-deployment validation.

  • Knowledge of cloud platforms (e.g., AWS Cloud) and their native ML services used for hosting, monitoring, and managing model endpoints.
  • Familiarity with containerization (Docker) and orchestration (Kubernetes) for scalable ML serving infrastructure.
  • Understanding of performance monitoring concepts, including latency tracking, model health indicators, and drift signals.

  • Knowledge of CI/CD processes, configuration templates, and automated operational workflows specific to ML systems.

  • Skills:

  • Proven expertise in MLOps, specifically managing model deployment, proactive monitoring, incident resolution, and performance tuning.

  • Ability to write and maintain automation scripts, validation utilities, and operational workflows to support ML pipelines.
  • Ability to collaborate effectively with DevOps, Data Science, and Platform Engineering teams to improve model reliability and system stability.
  • Skilled in applying structured software development methodologies (e.g., Agile/Scrum) to support platform enhancements and iterative delivery.
  • Strong analytical, troubleshooting, and root-cause diagnosis skills in production environments.

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