Job description

JOB DESCRIPTION

Job Title	ML Operations Lead
Job Level / Grade	E1 (Senior Manager)
Division	AI & Agent Operations (Day 2), AI Organisation
Reports To	Assistant Director, AI & Agent Operations (Singapore)
Location	India
Direct Reports	2 × ML Operations Engineer (India)
Role Focus	Traditional data-science / ML models — NOT LLM / GenAI

SECTION A: POSITION SUMMARY

State the objective and purpose of the role.

Lead the machine-learning operations team in India, operating the organisation’s traditional data-science / ML models in production — for example churn prediction, fraud and revenue assurance, and network capacity forecasting — across the consumer, enterprise (B2B) and network businesses. Own the discipline that keeps predictive models accurate and safe in production: drift monitoring, retraining and validation, deployment and rollback, lineage, and cost. Lead a team of two engineers and report to the Lead of AI & Agent Operations in Singapore, coordinating on change, incidents and standards. This role is classical MLOps.

SECTION B: KEY RESPONSIBILITIES AND RESULTS

Indicate key responsibilities and performance indicators of this role.

#	Key Responsibilities & Expected Results
1	Lead and develop the India ML operations team; set direction, standards and priorities for the model estate. Comfortable in translating Business requirements and communicating to his repartees. Preferred to hold prior experience in handling churn models.
2	Own the deployment and retraining discipline — validation gates (backtest / champion-challenger) before any model is promoted, with a rehearsed rollback path.
3	Own drift and performance monitoring across the portfolio (data, feature and concept drift), and the retraining triggers and thresholds per model.
4	Own the lineage registry (version, training data, baseline, last-known-good) and model auditability for the estate.
5	Own the responsibility of applying Business requested model changes in agreed timelines. Model responsibility — monitoring bias / fairness on decision-making models (e.g. churn, fraud) and keeping humans in the loop where appropriate.
6	Manage the partnership with Data Engineering — data contracts, freshness and change-notification — and treat data-quality issues as first-class.
7	Coordinate with the Singapore lead and team on change scheduling, incidents affecting shared services, and estate-wide standards.
8	Report model health, deployment success and portfolio performance into the function’s KPIs.

SECTION C: MAJOR CHALLENGES / TYPICAL PROBLEMS ENCOUNTERED

List the principal challenges or problems faced by the role in achieving the results of the position.

• Running the model estate from India while the overall function is led from Singapore — cross-border coordination on standards, change and incidents.

• Classic MLOps challenges — drift, model decay, retraining discipline and data-quality dependence — across a portfolio, with a lean team of three.

• Balancing model performance with responsibility (fairness, transparency) on models that drive customer and revenue decisions.

SECTION D: DECISION MAKING AUTHORITY

The scope and impact of the job and the extent of authority to manage resources and make decisions.

Decisions made under own authority

Decisions referred to higher authority

• Retraining and model-promotion decisions within the validation-gate discipline.

• India team priorities, standards and drift-response actions.

• Rollback to a previous model / safe baseline for models the team operates.

• Data-quality escalations to Data Engineering.

• Cross-function priorities and estate-wide standards (the Singapore Lead).

• Enterprise risk acceptance and any regulatory considerations on model use.

• Headcount, budget and procurement beyond delegated limits.

• Changes affecting the wider Day 1, data or platform functions.

SECTION E: QUALIFICATIONS / EXPERIENCE / KNOWLEDGE REQUIRED

Key knowledge and skills required for this role, and a suitable level of qualification where applicable.

Category	Essential for this role	Good to have
Education & Qualifications	• Degree in Computer Science, Data Science, Engineering, Statistics, or a related field	• Postgraduate qualification in ML/AI or a quantitative discipline
Work Experience	• 8+ years in ML / MLOps / data-science engineering, with team leadership • Hands-on experience deploying and operating traditional ML models in production • Experience with drift monitoring, retraining and model validation	• Telco-scale data / model environments. • Experience in customer churn. • Experience managing a team and cross-border coordination and comfortable in facing Business.
Technical / Professional Skills	• MLOps: CI/CD for models, model registries, lineage • Drift detection (data / feature / concept) and retraining pipelines • Champion-challenger / backtesting and validation discipline • Model performance metrics (AUC, precision-recall) and decay management	• Bias / fairness monitoring • Cloud ML platforms
Leadership & Soft Skills	• People leadership and mentoring • Disciplined operational judgment • Clear communication across a cross-border reporting line	• Stakeholder management with data and business teams
Other Task-Specific Knowledge	• Understanding of telco predictive use cases (churn, fraud, revenue assurance, network forecasting)	• Data governance and model-risk considerations

Department

AI Factory

Open Positions

Skills Required

MLOps,Customer Churn,Model Deployment,,Model Monitoring,Drift Detection,Model Retraining

Years Of Exp

20 to 25 years

Designation

ML Operations Engineer

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