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