Sr Machine Learning Engineer

Job Description & Requirements

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