Join EPAM Malaysia as a Senior Machine Learning Engineer and lead the charge in creating cutting-edge AI solutions that solve complex, real-world problems. You'll design and deploy scalable ML pipelines using tools like TensorFlow, PyTorch, Databricks and Snowflake, while harnessing the power of AWS, Azure, or GCP cloud platforms. Bring your passion for technology and problem-solving to EPAM and make an impact in driving the future of AI across industries.
Responsibilities
- Transition machine learning algorithms into production environments and integrate them with enterprise ecosystems, IoT devices and mobile platforms
- Design, build, and optimize the end-to-end ML lifecycle, including data preparation, feature extraction, model optimization, performance monitoring and testing strategies such as A/B, Canary, or Blue-Green testing
- Develop frameworks to enable data scientists to create production-grade ML models faster and more effectively
- Collaborate with data scientists and engineers to enhance the performance and scalability of ML pipelines
- Establish and maintain CI/CD/CT processes for ML systems while ensuring continuous training of models and early drift detection (e.g., data, concept, schema)
- Promote and implement MLOps best practices to ensure scalability, reliability, and maintainability of ML models
- Continuously identify and address technical risks, gaps and debt in ML systems while seeking opportunities for improvement with new tools and techniques
- Create comprehensive specifications, documentation and user guides for developed solutions to ensure seamless collaboration and deployment
Requirements
- At least 3 years of hands-on experience with Machine Learning
- Proficient in Python and SQL with hands-on experience in a modern data stack (e.g., Databricks, Snowflake, Spark, BigQuery)
- Expertise in common ML frameworks like TensorFlow, PyTorch, or Scikit-learn and MLOps platforms like Databricks MLflow, Kubeflow, or TFX
- Skilled in designing and managing scalable ML pipelines using tools such as Apache Spark or Airflow, and implementing CI/CD workflows
- Experience with a major cloud platform (AWS, Azure, or GCP) and containerization technologies (Docker, Kubernetes)
- Proven ability to identify risks, debug complex issues and work cross-functionally with data scientists, engineers and product teams
- Strong communication skills for articulating technical concepts and creating clear documentation
- English language proficiency at anUpper-Intermediatelevel(B2)or higher
