Machine Learning Engineer
AI Summary
●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.
About this role
●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.
Requirements
● 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.
Benefits
- Freedom of work
- Work-life balance
- Free meals and use of gym
- Opportunities for promotion
- Competitive salary
- Pay raise
- Start-up environment
- Multi-cultural and Agile work environment
- Diversified use of technology
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