Job Description
ML Operations Engineer
- Lead Department: AI & Agent Operations (Day 2), AI Organisation

Location:
Pune, India Reporting To: Assistant Director, AI & Agent Operations (Singapore) Team Size: 2 ML Operations Engineers Work Schedule: Rotational Shifts Domain: Information Technology (IT) Job Summary We are looking for an experienced ML Operations Lead to manage the deployment, monitoring, maintenance, and optimization of traditional machine-learning models in production. The role will oversee predictive models across consumer, enterprise (B2B), and network businesses, including customer churn prediction, fraud detection, revenue assurance, and network capacity forecasting. The successful candidate will lead a team of two engineers in India, ensuring model accuracy, reliability, governance, and operational efficiency while coordinating with the Singapore leadership team. This role focuses on classical MLOps and traditional machine-learning models, not LLMs or Generative AI.

Key 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. Required Qualifications & Experience
• Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, or a related discipline.
• 8+ 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. Preferred Qualifications




• 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. Key Competencies
• People leadership, mentoring, and team development.
• Robust 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. Decision-Making Responsibilities 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. Key Challenges
• 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.

📌 ML Operations Engineer (Pune)
🏢 YO IT CONSULTING
📍 Pune