Overview

ML Operations Engineer - Lead

Responsibilities

• Lead, mentor, and develop the India ML Operations team, establishing operational standards, priorities, and delivery timelines.
• Manage production deployment, retraining, validation, and rollback of traditional ML models using CI/CD pipelines and structured validation processes.
• Implement model monitoring to detect data, feature, and concept drift, model decay, and performance degradation; define appropriate retraining triggers and thresholds.
• Establish and maintain model registries and lineage records, including model versions, training datasets, performance baselines, and last-known-good versions.
• Ensure model changes requested by business stakeholders are implemented within agreed timelines and validated before deployment.
• Apply champion-challenger testing, backtesting, and validation gates to ensure model reliability before promoting models to production.
• Monitor model performance using appropriate metrics, including AUC, precision-recall, and other relevant evaluation measures.
• Oversee bias and fairness monitoring for decision-making models, ensuring appropriate transparency and human oversight where required.
• Collaborate with Data Engineering teams on data quality, data freshness, data contracts, and change notifications.
• Coordinate with the Singapore leadership team on deployment schedules, incident management, shared services, and operational standards.
• Report model health, deployment success, operational risks, and portfolio performance against defined KPIs.
• Ensure adherence to data governance, model auditability, and applicable model-risk management requirements.
• Independent authority :
• Manage India team priorities and operational standards.
• Initiate retraining, model promotion, and rollback decisions within approved validation procedures.
• Take appropriate drift-response actions and escalate data-quality issues to Data Engineering.
• Escalation to leadership :
• Enterprise risk acceptance and regulatory considerations.
• Cross-functional priorities and standards affecting the wider AI and data ecosystem.
• Headcount, budget, procurement, and changes impacting other platform or data functions.
• Maintaining model accuracy and reliability across a diverse production portfolio.
• Managing drift, model decay, and data-quality dependencies with a lean engineering team.
• Coordinating deployments, incidents, and standards across India and Singapore.
• Balancing predictive performance with fairness, transparency, and responsible model use.

Qualifications

• Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, or a related discipline.
• 18+ years of experience in ML engineering, MLOps, or data science engineering, including team leadership.
• Hands-on experience deploying, monitoring, and maintaining traditional machine-learning models in production.
• Strong experience with model drift detection, retraining pipelines, model validation, and production deployment.
• Proven experience with model CI/CD, model registries, lineage management, and rollback procedures.
• Experience with champion-challenger frameworks, backtesting, and model performance evaluation.
• Strong understanding of model governance, data quality, and bias/fairness monitoring.
• Experience leading engineering teams and communicating effectively with business and technical stakeholders.
• Experience working with telecommunications-scale data and production ML environments.
• Prior experience managing customer churn prediction models.
• Familiarity with predictive use cases such as fraud detection, revenue assurance, and network capacity forecasting.
• Experience with cloud-based machine-learning platforms.
• Postgraduate qualification in ML/AI, Data Science, or another quantitative discipline.
• Experience coordinating with international teams across geographies and time zones.
• People leadership, mentoring, and team development.
• Strong analytical and problem-solving skills.
• Sound operational judgment and incident management.
• Effective stakeholder communication and business requirement translation.
• Ability to prioritize competing demands and manage production risks.
• Strong coordination skills in a cross-border reporting environment.