ML Operations Engineer
Pune · Senior
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Job description
JOB DESCRIPTION
| Job Title | ML Operations Engineer |
| Job Level / Grade | P3 or P4 |
| Division | AI & Agent Operations (Day 2), AI Organisation |
| Reports To | ML Operations Lead (India) |
| Location | India |
| Role Focus | Traditional data-science / ML models — NOT LLM / GenAI |
| SECTION A: POSITION SUMMARY |
State the objective and purpose of the role.
Operate the organisation’s traditional data-science / ML models in production as part of the India ML operations team — executing deployments and retraining, monitoring for drift, maintaining lineage, and keeping predictive models (such as churn, fraud and network forecasting) accurate and safe. A hands-on execution role working to the standards and approvals set by the ML Operations Lead. This role is classic MLOps and does not cover LLM / GenAI agents.
| SECTION B: KEY RESPONSIBILITIES AND RESULTS |
Indicate key responsibilities and performance indicators of this role.
| # | Key Responsibilities & Expected Results |
| 1 | Execute model deployments and retraining through the pipeline under approved plans — packaging, validation runs, staged promotion. |
| 2 | Run drift monitoring (data / feature / concept) and produce the associated reporting; flag threshold breaches. |
| 3 | Validate models against the incumbent (backtest / champion-challenger) before promotion — no blind swaps. |
| 4 | Run optimization algorithms to select best intervention candidate against model predictions. |
| 5 | Maintain the lineage records (version, data, baseline, last-known-good) and keep them current. |
| 6 | Execute rollbacks to a previous model / safe baseline on instruction. |
| 7 | Produce model-performance, cost and quota reporting for the portfolio. |
| 8 | Raise data-quality issues to the lead and to Data Engineering promptly, with evidence. |
| SECTION C: MAJOR CHALLENGES / TYPICAL PROBLEMS ENCOUNTERED |
List the principal challenges or problems faced by the role in achieving the results of the position.
• Business driven targeted changes on intervention results produced by the models. • Model retraining and rerun cycles based on business updates. • Keeping models accurate as data and the world change; catching drift early. • Dependence on upstream data quality. |
| 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 |
• Deployment, retraining and monitoring execution within approved plans. • Lineage and reporting upkeep. • Timely, evidenced escalation of data issues. | • Model-promotion approval (the ML Operations Lead). • Retraining strategy and threshold changes. • Rollback decisions beyond agreed procedures. |
| 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, or a related field. | • ML / cloud certifications |
| Work Experience | • 4–7 years in MLOps / DevOps / data or ML engineering • Hands-on experience with churn predictive models and CI/CD pipelines and production model deployment. | • Drift monitoring / model-performance reporting experience • Telco or large-scale data environment |
| Technical / Professional Skills | • Python and CI/CD tooling • Model deployment, registry and lineage concepts • Data processing and reporting • Understanding of model validation and drift | • Champion-challenger / backtesting • Cloud ML services |
| Non-Technical / Soft Skills | • Precision, reliability and process discipline • Clear status communication across a cross-border team | • Collaboration with data teams |
| Other Task-Specific Knowledge | • Understanding of traditional predictive model concepts | • Awareness of telco use cases (churn, fraud, forecasting) |
Department
AIDA
Open Positions
1
Skills Required
MLOps,Customer Churn,Model Deployment,Drift Detection,Model Retraining,Python
Role
JOB DESCRIPTION
| Job Title | ML Operations Engineer |
| Job Level / Grade | P3 or P4 |
| Division | AI & Agent Operations (Day 2), AI Organisation |
| Reports To | ML Operations Lead (India) |
| Location | India |
| Role Focus | Traditional data-science / ML models — NOT LLM / GenAI |
| SECTION A: POSITION SUMMARY |
State the objective and purpose of the role.
Operate the organisation’s traditional data-science / ML models in production as part of the India ML operations team — executing deployments and retraining, monitoring for drift, maintaining lineage, and keeping predictive models (such as churn, fraud and network forecasting) accurate and safe. A hands-on execution role working to the standards and approvals set by the ML Operations Lead. This role is classic MLOps and does not cover LLM / GenAI agents.
| SECTION B: KEY RESPONSIBILITIES AND RESULTS |
Indicate key responsibilities and performance indicators of this role.
| # | Key Responsibilities & Expected Results |
| 1 | Execute model deployments and retraining through the pipeline under approved plans — packaging, validation runs, staged promotion. |
| 2 | Run drift monitoring (data / feature / concept) and produce the associated reporting; flag threshold breaches. |
| 3 | Validate models against the incumbent (backtest / champion-challenger) before promotion — no blind swaps. |
| 4 | Run optimization algorithms to select best intervention candidate against model predictions. |
| 5 | Maintain the lineage records (version, data, baseline, last-known-good) and keep them current. |
| 6 | Execute rollbacks to a previous model / safe baseline on instruction. |
| 7 | Produce model-performance, cost and quota reporting for the portfolio. |
| 8 | Raise data-quality issues to the lead and to Data Engineering promptly, with evidence. |
| SECTION C: MAJOR CHALLENGES / TYPICAL PROBLEMS ENCOUNTERED |
List the principal challenges or problems faced by the role in achieving the results of the position.
• Business driven targeted changes on intervention results produced by the models. • Model retraining and rerun cycles based on business updates. • Keeping models accurate as data and the world change; catching drift early. • Dependence on upstream data quality. |
| 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 |
• Deployment, retraining and monitoring execution within approved plans. • Lineage and reporting upkeep. • Timely, evidenced escalation of data issues. | • Model-promotion approval (the ML Operations Lead). • Retraining strategy and threshold changes. • Rollback decisions beyond agreed procedures. |
| 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, or a related field. | • ML / cloud certifications |
| Work Experience | • 4–7 years in MLOps / DevOps / data or ML engineering • Hands-on experience with churn predictive models and CI/CD pipelines and production model deployment. | • Drift monitoring / model-performance reporting experience • Telco or large-scale data environment |
| Technical / Professional Skills | • Python and CI/CD tooling • Model deployment, registry and lineage concepts • Data processing and reporting • Understanding of model validation and drift | • Champion-challenger / backtesting • Cloud ML services |
| Non-Technical / Soft Skills | • Precision, reliability and process discipline • Clear status communication across a cross-border team | • Collaboration with data teams |
| Other Task-Specific Knowledge | • Understanding of traditional predictive model concepts | • Awareness of telco use cases (churn, fraud, forecasting) |
Years Of Exp
8 to 11 years
Designation
ML Operations Engineer
tags
MLOps,Customer Churn,Model Deployment,Drift Detection,Model Retraining,Python
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