#SGunited Jobs Senior Machine Learning Engineer

Job SummaryResponsible for managing MLOps workflows, tools, and production support processes to ensure the stability, reliability, and performance of ML models and pipelines throughout their lifecycle.ResponsibilitiesDesign, implement, and manage MLOps workflows, tools, and operational processes for ML and GenAI solutions to ensure efficient production deploymentOversee daily stability, reliability, and operational health of ML models and pipelines to maintain consistent performanceManage model lifecycle operations including registration, versioning, deployment tracking, lineage, reproducibility, and governance to ensure compliance and traceabilityImplement monitoring systems for data drift, concept drift, model performance degradation, inference quality, and service-level issues to proactively detect anomaliesDevelop and maintain dashboards and alerting mechanisms to track model health, data quality, inference behavior, and operational metrics for timely insightsCreate and execute incident handling, recovery, rollback, and escalation procedures for ML-related issues to minimize downtime and impactPlan and implement data quality controls, dataset versioning, and lineage tracking solutions across the ML lifecycle to support data governanceSupport data governance initiatives by documenting controls, policies, and practices related to ML models, datasets, and production usageCommunicate complex technical risks and solutions clearly to non-technical stakeholders to facilitate informed decision-makingApply analytical and pragmatic approaches to translate governance principles into actionable implementation plansAdapt proactively and work independently in a fast-paced environment to meet evolving operational demandsPreferred competencies and qualificationsBachelor’s or Master’s degree in Computer Science or related fieldHands-on experience with MLOps tools and cloud ML platforms such as MLflow, Databricks, Azure ML, or equivalentsProficient in SQL and data analysis for validation, troubleshooting, monitoring, and reporting purposesExperience with visualization and alerting tools such as Power BI, Tableau, or Databricks SQL dashboardsFamiliarity with Git, CI/CD pipelines, scripting, and production support best practicesKnowledge of ML and data development processes in the telecommunications environment