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Senior Machine Learning Developer
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Machine learning engineers design and develop AI and machine learning models to solve real-world problems. They must have a strong background in software engineering, data analysis, and statistics.
The ideal candidate will have experience with machine learning frameworks such as Scikit-learn, XGBoost, PyTorch, or TensorFlow. They should also be familiar with AWS services including SageMaker, Glue, Lambda, Step Functions, S3, and CloudWatch.
Key Responsibilities:- Design, build, and maintain scalable end-to-end machine learning pipelines using AWS services.
- Automate model training, hyperparameter tuning, deployment, and monitoring for production workloads.
- Work with data scientists to operationalize models and implement automated unit, integration, and regression testing frameworks to validate performance and robustness before deployment.
- Develop reusable feature pipelines in collaboration with data engineers, integrating with feature stores and real-time/batch data sources.
- Deploy models for real-time and batch inference using SageMaker endpoints and monitor for latency, drift, and performance degradation using CloudWatch and Prometheus.
- At least 5 years of experience as an ML Engineer, MLOps Engineer, or equivalent.
- Strong proficiency in Python and machine learning frameworks.
- Proven experience with AWS ML services and CI/CD tools.
- Familiarity with infrastructure-as-code, versioning, and model registries.
