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Ml Engineer
2 weeks ago
Role & responsibilities
- Design, develop, and optimize scalable ML pipelines using Python and modern ML engineering tools.
- Implement feature stores, model versioning, and model serving using tools like Feast, Kubeflow, and MLflow.
- Work closely with data scientists to productize machine learning models in production environments.
- Write and maintain Dockerfiles and Kubernetes YAML configurations to containerize and deploy models and services.
- Apply performance tuning and optimization techniques to improve ML pipeline efficiency.
- Work in Big Data environments and integrate with data platforms when required.
- Collaborate with cross-functional teams including data engineering, DevOps, and product teams to deliver ML solutions.
- Maintain documentation, ensure version control, and follow best practices for reproducibility and model governance.
Preferred candidate profile
- 5+ years of experience in Python programming with focus on performance tuning and optimization.
- Proficiency in NumPy, Pandas, and data frame operations.
- Hands-on experience with Feast, Kubeflow, and MLflow.
- Solid understanding of Docker and Kubernetes; ability to write and manage YAML configurations.
- Working knowledge of Big Data environments (Spark, Hadoop, etc.) is a plus.
- Strong analytical and problem-solving skills with meticulous attention to detail.
- Excellent communication and interpersonal skills; ability to thrive in a fast-paced and collaborative environment.
- Solid understanding of machine learning and data science fundamentals.
