Amgen is looking to build and scale machine learning models from development to production, requiring expertise in both machine learning and operations to create efficient and reliable ML pipelines.
Requirements
- Solid foundation in machine learning algorithms and techniques
- Experience in MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow); Experience in DevOps tools (e.g., Docker, Kubernetes, CI/CD)
- Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn)
- Experience with big data technologies (e.g., Spark, Hadoop), and performance tuning in query and data processing
- Experience with data engineering and pipeline development
- Experience in statistical techniques and hypothesis testing, experience with regression analysis, clustering and classification
- Knowledge of NLP techniques for text analysis and sentiment analysis
Responsibilities
- Collaborate with data scientists to develop, train, and evaluate machine learning models.
- Build and maintain MLOps pipelines, including data ingestion, feature engineering, model training, deployment, and monitoring.
- Leverage cloud platforms (AWS, GCP, Azure) for ML model development, training, and deployment.
- Implement DevOps/MLOps best practices to automate ML workflows and improve efficiency.
- Develop and implement monitoring systems to track model performance and identify issues.
- Conduct A/B testing and experimentation to optimize model performance.
- Work closely with data scientists, engineers, and product teams to deliver ML solutions.
Other
- Excellent analytical and troubleshooting skills.
- Strong verbal and written communication skills
- Ability to work effectively with global, virtual teams
- High degree of initiative and self-motivation.
- Ability to manage multiple priorities successfully.
