Machine Learning Engineer 4

Job description

What You’ll Do: 
 Develop classifiers, predictive models and multi variate optimization algorithms on
large-scale datasets using advanced statistical modeling, machine learning and data
mining.
 Design, implement and operate scalable models that can work with large-scale datasets
(100s billions of records) in production systems.
 Ability to articulate the design and implementation choices to cross functional teams
 R&D will revolve around a few key focus areas such as Agentic AI solutions, predictive
models for conversion optimization, Reinforcement Learning, and Forecasting &
Planning.
 Model Lifecycle Management: Manage model versioning, deployment strategies,
rollback mechanisms, and A/B testing frameworks. Coordinate model registries, artifacts,
and promotion workflows in collaboration with ML Engineers Develop CI/CD and
orchestration workflows using GitLab CI, GitHub Actions, CircleCI, Airflow, Argo
Workflows, or similar tools.
 Review and optimize data science models, including code refactoring, containerization,
deployment, versioning, and performance tuning. Implement model testing, validation,
and automated QA pipelines, ensuring reproducibility and compliance.
 Monitor models in production, including data drift, concept drift, performance
degradation, and system reliability.
 Collaborate multi-functionally with data scientists, data engineers, and architects; build
documentation and improve team processes.
 Ensure governance, security, and compliance for ML pipelines (access controls, audit
logs, model reproducibility, lineage).