Develop and maintain large-scale distributed machine learning systems using frameworks like TensorFlow, PyTorch, and Scikit-Learn
Build predictive models including churn prediction, user journey analysis, and sales forecasting using behavioral data
Work with supervised and unsupervised learning, survival analysis, time series modeling, and statistical forecasting techniques
Collaborate with business units to understand their ML needs
Optimize feature extraction, transformation, and selection while managing Feature Stores for reusability across ML pipelines
Strong focus on MLOps practices including model training, versioning, monitoring, and deployment using CI/CD pipelines, Docker, Kubernetes, Airflow, SageMaker, and MLflow
Ensure scalability, reliability, cost efficiency, and ease of use of the machine learning platform while maintaining model observability and connecting outcomes to product and strategic goals

5+ years Machine Learning Engineering experience building production ML systems
Strong experience with supervised and unsupervised learning, survival analysis, time series modeling, and statistical forecasting
Skilled in building models such as churn prediction, user journey analysis, and sales forecasting using behavioral data
Expert with TensorFlow, PyTorch, or Scikit-Learn for model development
Experienced in model training, versioning, deployment, and monitoring in production
Solid background in CI/CD pipelines, Docker, Kubernetes, Apache Airflow, AWS SageMaker, MLflow, and model observability tools
Experience with feature stores and optimizing feature extraction, transformation, and selection
Ability to develop large-scale distributed ML systems that are scalable, performant, and reliable
Ability to connect model outcomes to product goals and strategic business objectives
Experience working with business units and cross-functional teams to deploy and integrate ML models
Fluent English (mandatory)

Senior Machine Learning Engineer – MLOps, TensorFlow

Key skills