ML Operations Lead

Role: ML Operations Lead (MLOps Lead)

Job Level: Senior Manager

Summary

We are looking for an experienced ML Operations Lead (MLOps Lead) to lead the deployment, monitoring, maintenance, and continuous improvement of traditional Machine Learning (ML) models in production environments. The ideal candidate will have strong hands-on expertise in classical MLOps, model deployment, model monitoring, drift detection, model retraining, and model validation, along with experience leading a technical team.

The role focuses on maintaining the reliability, accuracy, stability, and performance of production ML models across use cases such as customer churn prediction, fraud detection, revenue assurance, and network capacity forecasting. This role focuses on traditional Machine Learning and classical MLOps, not Large Language Models (LLMs) or Generative AI.

Key Responsibilities

• Lead, mentor, and manage a team of ML Operations Engineers.
• Manage end-to-end model deployment, monitoring, retraining, and rollback.
• Implement drift detection for data, features, and model performance.
• Establish model validation using backtesting and champion-challenger approaches.
• Manage ML CI/CD pipelines, model registries, versioning, and lineage.
• Monitor model performance using metrics such as AUC, precision, and recall.
• Collaborate with business, data engineering, and cross-functional teams.
• Ensure model reliability, data quality, and timely delivery of business requirements.

Mandatory Skills

• Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, or a related discipline.
• 8+ years of experience in Machine Learning, MLOps, Data Science Engineering, or related fields, including team leadership.
• Hands-on experience deploying and operating traditional ML models in production.
• Strong experience in customer churn prediction and customer analytics.
• Expertise in model deployment, model monitoring, drift detection, and model retraining.
• Experience building and managing ML CI/CD pipelines and model registries.
• Strong understanding of model versioning, lineage, reproducibility, and rollback strategies.
• Practical experience with data drift, feature drift, concept drift, and model performance decay.
• Experience with champion-challenger testing, backtesting, and model validation.
• Strong understanding of model evaluation metrics, including AUC, precision, and recall.
• Experience designing and maintaining automated retraining and validation pipelines.
• Demonstrated people-management, mentoring, and team-leadership capabilities.
• Excellent communication skills and the ability to work effectively with business and technical stakeholders.
• Strong operational judgment and the ability to manage production incidents and competing priorities.