Machine Learning Specialist {LLM}

NCS Group; Taguig; On-site; PHP 669,600 - 1,116,000; Full time

Job summary

NCS Group in Metro Manila, Taguig is looking for a skilled MLOps Engineer to execute performance tuning on model serving infrastructure and enhance operational pipelines. You will ensure model prediction stability and support CI/CD processes.

The ideal candidate must have a solid cloud background with expertise in AWS, GCP, or Azure, and at least 2 years of hands-on experience in MLOps or Data Engineering. Strong skills in Docker and Kubernetes are essential for this role.

Qualifications

• Minimum of 2+ years hands-on experience in a production environment covering MLOps, Data Engineering, or Software Engineering.
• Proven ability to meet strict Service Level Agreements (SLAs) related to system uptime and incident response.
• Experience supporting cloud-hosted ML systems in distributed, high-availability environments.

Responsibilities

• Execute performance tuning activities for model serving infrastructure.
• Conduct post-deployment validation checks to ensure model prediction stability.
• Support enhancement of operational pipelines including CI/CD workflows.

Skills

MLOps

Cloud platforms (AWS, GCP, Azure)

SQL

Containerization (Docker and Kubernetes)

Automation scripts and workflows

Agile/Scrum methodologies

Job description

Responsibilities

• 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.

Minimum Qualifications

• Machine Learning Operations (MLOPS)
• With Cloud background- AWS, GCP, Azure, Alibaba etc
• Understanding SQL (Data Pipelines)/ Data Engineering
• Containerization (Docker and Kubernetes)

Other Qualifications

• 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.