
Principal Machine Learning Specialist
5 days ago
Job Summary:
Develop and implement predictive models that drive business outcomes.
Collaborate with cross-functional teams to define data requirements and success metrics.
Deploy machine learning models into production using best practices to ensure scalability and reliability.
Key Responsibilities:
- Design, develop, and validate predictive and analytical models.
- Work closely with stakeholders to identify key performance indicators.
- Deploy models using scalable architecture for real-time model serving.
- Implement automated pipelines for model training, testing, and retraining.
Technical Skills Required:
- Strong proficiency in Python, including libraries like Pandas, NumPy, Scikit-learn, and PyTorch/TensorFlow.
- Experience with ML Ops tools/frameworks, including MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, or Azure ML.
- Good understanding of data engineering concepts, including ETL processes, APIs, and containerization/orchestration.
- Hands-on experience with cloud platforms like AWS, GCP, or Azure for ML model deployment.
- Strong background in statistics, machine learning algorithms, and model evaluation techniques.
- Familiarity with version control (Git) and CI/CD pipelines.
Preferred Qualifications:
- Bachelor's/Master's degree in Computer Science, Data Science, Statistics, or related field.
- Experience in handling large-scale data pipelines and real-time model serving.
- Exposure to feature stores and automated model monitoring.
- Strong problem-solving, analytical, and communication skills.
Key Requirements:
- Optimize model performance and ensure continuous improvement.
- Monitor model drift, performance degradation, and ensure scalability and reliability.
- Implement CI/CD practices for machine learning workflows.
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