Operating Manager – AI & Machine Learning Engineering

Gurgaon - India

Monthly Salary: Not Disclosed

Posted on: 7 hours ago

Vacancies: 1 Vacancy

Job Summary

Team Leadership and Talent Development

Manage coach and grow a high-performing team of machine learning engineers promoting a culture of innovation collaboration and continuous learning.
Provide technical direction architectural oversight and career mentorship.
Define team objectives and success metrics aligned with enterprise priorities.

Program Execution and Delivery

Drive the successful execution of ML use cases such as customer churn prediction upsell opportunity scoring demand forecasting and operational risk detection.
Work closely with data science data engineering product and business stakeholders to define and deliver scalable ML solutions.
Oversee delivery timelines model development deployment readiness and feedback integration.

ML Engineering and MLOps Strategy

Establish best practices in model development deployment and monitoring using tools like MLflow SageMaker Azure ML Airflow or Kubeflow.
Guide the team in implementing CI/CD for ML pipelines model versioning feature stores and performance monitoring.
Champion a strong foundation in software engineering code quality and reusability in ML development.

Functional & Cross-Domain Focus

Align ML efforts with key business domains such as Sales (lead scoring renewals) Service (case triage) Finance (forecasting) Order Fulfillment (ETA risk) and Supply Chain (inventory planning logistics optimization).
Collaborate with business owners to prioritize high-impact ML use cases and ensure adoption and value realization.
1. Technology & Architecture Oversight
- Partner with data platform and infrastructure teams to scale ML solutions using Snowflake Datarobots and enterprise cloud platforms (AWS Azure GCP).
- Ensure ML models integrate seamlessly with business systems such as Salesforce Oracle Fusion Cloud and other operational tools.

Required:

8 years of experience in machine learning data science or engineering roles with 3 years in a technical leadership or management capacity.
Proven experience building and deploying machine learning solutions in production environments.
Hands-on background with Python ML frameworks (scikit-learn PyTorch TensorFlow) and orchestration tools.
Strong understanding of MLOps practices model lifecycle management and pipeline automation.
Experience working with cross-functional stakeholders to deliver ML-powered business solutions.

Preferred:

Experience supporting business functions such as Sales Finance or Supply Chain with applied ML.
Familiarity with cloud platforms (AWS Azure or GCP) and enterprise data tools (Snowflake dbt Matillion).
Exposure to enterprise platforms such as Oracle Fusion Cloud Salesforce or ServiceNow.

Team Leadership and Talent Development Manage coach and grow a high-performing team of machine learning engineers promoting a culture of innovation collaboration and continuous learning. Provide technical direction architectural oversight and career mentorship. Define team objectives and success...

Team Leadership and Talent Development

Manage coach and grow a high-performing team of machine learning engineers promoting a culture of innovation collaboration and continuous learning.
Provide technical direction architectural oversight and career mentorship.
Define team objectives and success metrics aligned with enterprise priorities.

Program Execution and Delivery

Drive the successful execution of ML use cases such as customer churn prediction upsell opportunity scoring demand forecasting and operational risk detection.
Work closely with data science data engineering product and business stakeholders to define and deliver scalable ML solutions.
Oversee delivery timelines model development deployment readiness and feedback integration.

ML Engineering and MLOps Strategy

Establish best practices in model development deployment and monitoring using tools like MLflow SageMaker Azure ML Airflow or Kubeflow.
Guide the team in implementing CI/CD for ML pipelines model versioning feature stores and performance monitoring.
Champion a strong foundation in software engineering code quality and reusability in ML development.

Functional & Cross-Domain Focus

Align ML efforts with key business domains such as Sales (lead scoring renewals) Service (case triage) Finance (forecasting) Order Fulfillment (ETA risk) and Supply Chain (inventory planning logistics optimization).
Collaborate with business owners to prioritize high-impact ML use cases and ensure adoption and value realization.
1. Technology & Architecture Oversight
- Partner with data platform and infrastructure teams to scale ML solutions using Snowflake Datarobots and enterprise cloud platforms (AWS Azure GCP).
- Ensure ML models integrate seamlessly with business systems such as Salesforce Oracle Fusion Cloud and other operational tools.

Required:

8 years of experience in machine learning data science or engineering roles with 3 years in a technical leadership or management capacity.
Proven experience building and deploying machine learning solutions in production environments.
Hands-on background with Python ML frameworks (scikit-learn PyTorch TensorFlow) and orchestration tools.
Strong understanding of MLOps practices model lifecycle management and pipeline automation.
Experience working with cross-functional stakeholders to deliver ML-powered business solutions.

Preferred:

Experience supporting business functions such as Sales Finance or Supply Chain with applied ML.
Familiarity with cloud platforms (AWS Azure or GCP) and enterprise data tools (Snowflake dbt Matillion).
Exposure to enterprise platforms such as Oracle Fusion Cloud Salesforce or ServiceNow.