Job Summary
Responsible for managing MLOps workflows, tools, and production support processes to ensure the stability, reliability, and performance of ML models and pipelines throughout their lifecycle.
Responsibilities
- Design, implement, and manage MLOps workflows, tools, and operational processes for ML and GenAI solutions to ensure efficient production deployment
- Oversee daily stability, reliability, and operational health of ML models and pipelines to maintain consistent performance
- Manage model lifecycle operations including registration, versioning, deployment tracking, lineage, reproducibility, and governance to ensure compliance and traceability
- Implement monitoring systems for data drift, concept drift, model performance degradation, inference quality, and service-level issues to proactively detect anomalies
- Develop and maintain dashboards and alerting mechanisms to track model health, data quality, inference behavior, and operational metrics for timely insights
- Create and execute incident handling, recovery, rollback, and escalation procedures for ML-related issues to minimize downtime and impact
- Plan and implement data quality controls, dataset versioning, and lineage tracking solutions across the ML lifecycle to support data governance
- Support data governance initiatives by documenting controls, policies, and practices related to ML models, datasets, and production usage
- Communicate complex technical risks and solutions clearly to non-technical stakeholders to facilitate informed decision-making
- Apply analytical and pragmatic approaches to translate governance principles into actionable implementation plans
- Adapt proactively and work independently in a fast-paced environment to meet evolving operational demands
Preferred competencies and qualifications
- Bachelor's or Master's degree in Computer Science or related field
- Hands-on experience with MLOps tools and cloud ML platforms such as MLflow, Databricks, Azure ML, or equivalents
- Proficient in SQL and data analysis for validation, troubleshooting, monitoring, and reporting purposes
- Experience with visualization and alerting tools such as Power BI, Tableau, or Databricks SQL dashboards
- Familiarity with Git, CI/CD pipelines, scripting, and production support best practices
- Knowledge of ML and data development processes in the telecommunications environment
