<div>Umanist Staffing LLC A Small Business Enterprise (SBE); as per the gov of the USA, is the global advisor on niche skills workforce solutions. </div>

Umanist Staffing LLC

# Job Title: ML Engineering Lead
**Experience:** 19–27 Years overall **Relevant Experience:** 9+ years in ML, MLOps, or Data Science Engineering, including team leadership&nbsp; **Location:**&nbsp; Pune **Work Mode:** 5 Days Work from Office **Working Hours:** IST(870) **Notice Period:** Immediate Joiners to Serving Notice Period Candidates(only upto 30days)
## Role Overview
We are seeking an experienced ML Engineering Lead to lead a team responsible for operating and maintaining traditional Machine Learning models in production. The role focuses on ensuring model accuracy, reliability, stability, auditability, and continuous performance through effective monitoring, drift detection, retraining, validation, deployment, and rollback practices.
The successful candidate will manage a team of two ML Operations Engineers in India and collaborate with international stakeholders, Data Engineering teams, and business teams to ensure timely and reliable model operations.
**Note:** This is a classical MLOps role focused on traditional predictive ML models, not LLM/GenAI operations.
## Key Responsibilities
- Lead, mentor, and develop the ML Operations team, establishing operational standards, priorities, and best practices. - Manage the deployment, monitoring, retraining, validation, and maintenance of traditional ML models in production. - Establish model validation gates, including backtesting and champion-challenger approaches, before promoting models to production. - Define and manage rollback procedures to restore previous stable model versions when required. - Monitor data drift, feature drift, concept drift, and model performance degradation across the model portfolio. - Define retraining triggers, thresholds, and processes to maintain model accuracy and reliability. - Maintain model registries, version history, training data lineage, baseline metrics, and last-known-good model records. - Track model performance using relevant metrics such as AUC, precision, recall, and other business-aligned KPIs. - Monitor model bias and fairness, ensuring appropriate controls and human oversight for sensitive decision-making use cases. - Collaborate with Data Engineering teams to manage data quality, data freshness, data contracts, and upstream data changes. - Implement business-requested model changes within agreed timelines while maintaining operational and validation standards. - Coordinate with international stakeholders on deployment schedules, production incidents, change management, and operational standards. - Report model health, deployment success rates, retraining outcomes, and overall portfolio performance. - Support predictive ML use cases such as customer churn prediction, fraud detection, revenue assurance, and network capacity forecasting.
## Must-Have Skills
- Strong experience in **MLOps and production ML operations**. - Hands-on experience deploying and operating traditional Machine Learning models in production. - Strong understanding of **customer churn prediction models** and related predictive analytics use cases. - Expertise in model deployment, model monitoring, drift detection, and model retraining. - Experience implementing ML CI/CD pipelines, model registries, and model lineage. - Practical experience with data drift, feature drift, concept drift, and model decay management. - Experience with model validation, backtesting, and champion-challenger methodologies. - Knowledge of model performance metrics, including AUC, precision, recall, and precision-recall evaluation. - Experience leading teams, mentoring engineers, and managing operational priorities. - Strong communication and stakeholder-management skills, including cross-border collaboration. - Ability to translate business requirements into operational priorities and deliver model changes within agreed timelines. - Degree in Computer Science, Data Science, Engineering, Statistics, or a related discipline.
## Nice-to-Have Skills
- Experience in telecommunications (telco) or large-scale data environments. - Exposure to fraud detection, revenue assurance, and network capacity forecasting models. - Experience with cloud-based Machine Learning platforms. - Postgraduate qualification in Machine Learning, AI, Statistics, or another quantitative discipline. - Experience with model governance, auditability, responsible AI, bias monitoring, and model-risk management.
## Leadership Expectations
- Ability to lead a lean ML Operations team and establish scalable operating practices. - Strong operational judgment and ownership of production model health. - Effective coordination with business, data, and platform stakeholders. - Excellent verbal and written communication skills.
## Candidate Requirements
- Minimum 2 years of stability in an organization. - Immediate joiners and candidates currently serving notice are preferred. - Candidates based in Pune are preferred. - Willingness to work from the office five days a week in Pune. - Excellent communication skills are essential due to regular stakeholder and client interaction.