Machine Learning Operations Specialist  

Role Description

We are seeking a dedicated and skilled Machine Learning Operations Specialist to join our team. In this full-time, hybrid role (partly based in Taguig with work-from-home flexibility), you will design, implement, and optimize machine learning models for business applications.

Qualifications:

Model Monitoring and Maintenance

• Monitor performance, health, and operational status of ML models deployed to production.
• Detect anomalies, data drift, concept drift, or degradation in prediction quality.
• Implement corrective actions or coordinate with Data Science teams for retraining or model refinement.

Incident Management

• Triage and diagnose issues affecting model availability, accuracy, latency, or data quality.
• Execute immediate stabilization tasks or escalate to appropriate teams when needed.
• Document all incidents, resolutions, and preventive actions.

Deployment and Release Management

• Plan and execute rollout of new model versions, platform updates, or configuration changes.
• Validate deployments through regression checks, functional testing, and performance validation.
• Manage model versioning, rollback procedures, and release documentation.

Model Lifecycle Operations

• Manage routine operational tasks such as data refreshes, threshold tuning, and configuration updates.
• Automate workflows for recurring operational tasks where applicable.
• Support periodic model retraining cycles initiated by Data Science or ML Engineering teams.

Model Management Platform Support

• Support the enhancement and maintenance of the Model Management Platform, including model registry, monitoring tools, lineage dashboards, and pipeline components.
• Collaborate in implementing CI/CD pipelines for ML models (training, validation, deployment).
• Integrate new tools or operational improvements to enhance model reliability and observability.

Operational Stability and Risk Mitigation

• Address high-impact issues due to upstream data inconsistencies, environment drift, or infrastructure instability.
• Identify operational risks and propose preventive measures to ensure model uptime, accuracy, and compliance.
• Monitor and optimize resource usage related to ML tooling, serving infrastructure, and pipelines.

Cross-Functional Collaboration

• Work closely with Data Scientists, ML Engineers, DevOps, SysOps, and Platform teams to maintain seamless ML
• operations.
• Participate in post-incident reviews and recommend improvements for pipeline reliability, monitoring, and tooling.

• Statistical modeling, ML theory, and practical ML applications.
• Model interpretability and bias mitigation techniques.
• Data preprocessing methods (handling missing values, normalization, outlier detection).
• Anomaly detection algorithms (Isolation Forest, One-Class SVM, Autoencoders).
• Forecasting techniques (ARIMA, LSTM, Prophet).
• Model drift detection and mitigation strategies.
• Time-series analysis for predictive modeling.
• ML frameworks (TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost).