We are seeking a highly skilled Machine Learning Engineer to join a team focused exclusively on building and deploying machine learning models—not data science.
The ideal candidate will have hands-on experience with ML engineering, MLops, Amazon SageMaker, and Python, and will be responsible for developing scalable ML pipelines and production-ready solutions.
Key Responsibilities
- Design, build, and deploy machine learning models in production environments.
- Develop and maintain ML pipelines using Amazon SageMaker and other cloud-native tools.
- Implement MLops practices for model versioning, monitoring, and automation.
- Collaborate with software engineers and data engineers to integrate ML models into applications.
- Optimize model performance and scalability for real-time and batch inference.
- Ensure compliance with best practices in security, reliability, and maintainability.
- Write clean, efficient, and well-documented Python code for ML workflows.
Required Qualifications- Strong hands-on experience with Amazon SageMaker.
- Proficiency in Python for ML development and automation.
- Experience implementing MLops practices in production environments.
- Solid understanding of machine learning model lifecycle and deployment strategies.
- 5+ years of experience in machine learning engineering or related roles.
- Familiarity with cloud platforms (AWS preferred).
Preferred Qualifications- Experience with containerization (e.g., Docker) and orchestration tools (e.g., Kubernetes).
- Familiarity with CI/CD pipelines for ML workflows.
- Exposure to monitoring tools and model performance tracking.
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