Senior Machine Learning Engineer

Position Summary Responsible for managing MLOps workflows, tools, and production support processes for ML solutions. Ensure day-to-day stability, reliability, and performance of ML models and pipelines. Manage model lifecycle controls, including versioning, lineage, reproducibility, monitoring, and governance. Develop incident handling, recovery, and escalation procedures for ML-related issues. Support data quality, lineage tracking, and governance practices across the ML lifecycle. Strong MLOps and production operations focus Make an Impact by: Responsible for designing, implementing, and managing MLOps workflows, tools, and operational processes for ML and GenAI solutions. Oversee the day-to-day stability, reliability, and operational health of ML models and ML pipelines. Manage model lifecycle operations, including model registration, versioning, deployment tracking, lineage, reproducibility, and governance. Implement monitoring for data drift, concept drift, model performance degradation, inference quality, and service-level issues. Set up dashboards and alerts to track model health, data quality, inference behaviour, and operational metrics. Develop and execute incident handling, recovery, rollback, and escalation plans for ML-related issues. Plan and implement data quality, dataset versioning, and lineage tracking solutions across the ML lifecycle. Support data governance discussions, documentation, controls, and policies relating to ML models, datasets, and production usage. Skills for Success: Bachelor’s or Master’s degree in Computer Science or a related field Experience with MLOps processes and tools Experience with SQL, Databricks, MLFlow and PowerBI/Tableau. Hands-on experience with MLOps tools and cloud ML platforms such as MLflow, Databricks, Azure ML, or equivalent. Strong SQL and data analysis skills for validation, troubleshooting, monitoring, and reporting. Experience with model lifecycle management, including model registry, versioning, lineage, reproducibility, deployment tracking, and monitoring. Familiarity with dashboards and alerting tools such as Power BI, Tableau, Databricks SQL dashboards, or equivalent. Working knowledge of Git, CI/CD, scripting, and production support practices would be advantageous. Analytical and pragmatic, with the ability to interpret governance principles into implementation plans Clear communicator who can explain complex technical risks and solutions to non-technical stakeholders Self-driven and proactive, comfortable working in a fast-paced environment Familiarity with ML and data development process in telco environment