Collaborate with Machine Learning Engineers, Backend Engineers, Data Scientists, and Product Managers to develop AI-powered features and services.
Build, deploy, and maintain production-ready machine learning models that improve allocation, ETA prediction, demand forecasting, and marketplace optimization.
Develop scalable data processing, feature engineering, and model training pipelines for large-scale datasets.
Integrate machine learning models into backend services and ensure reliable, low-latency inference in production
Monitor model performance, identify model drift, and continuously improve model accuracy and operational reliability.
Contribute to MLOps practices including model versioning, automated deployment, monitoring, and experimentation.
Write clean, maintainable, and well-tested code while following software engineering best practices.
Participate in technical discussions, code reviews, and knowledge sharing to continuously improve the team's engineering standards.

Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field.
At least 3 years of professional experience building and deploying machine learning applications in production.
Strong proficiency in Python and experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
Experience with SQL and data processing frameworks for working with large datasets.
Familiarity with model deployment, REST APIs, Docker, Kubernetes, and cloud-based infrastructure.
Understanding of MLOps concepts including model lifecycle management, monitoring, CI/CD, and experimentation.
Solid software engineering fundamentals including version control, testing, debugging, and system design.
Strong analytical, problem-solving, and communication skills with the ability to work effectively in cross-functional teams.

Machine Learning Engineer

Key skills