Machine Learning and Artificial Intelligence
- Understanding of standard machine learning principles and deep learning concepts
- Utilizing classical machine learning and deep learning techniques to develop predictive models for determining clinically relevant outputs
- Developing scalable data ingestion and model training pipelines.
- Deploying production-level code in a cloud-based environment.
- Writing production-ready code that is version-controlled, readable, efficient, and well tested.
- Proficiency with Tensorflow / Pytorch, Dask / Kafka / Spark
- Experienced in design, training, and deploying various deep learning models
- Proficiency with Python and popular data analysis packages (Pandas, Numpy, Scikit Learn, Tensorflow/Pytorch, etc.)
- Understanding or desire to learn end to end Machine Learning technology stack (Tools such as GCP Vertex AI, Domino Data Labs, Kubernetes, ,Tensorflow, Pyspark, Jupyter Notebook, Hive, Hadoop, etc).
- Strong soft skills of communication , ability to share/teach others, work collaboratively with others etc.
- Proficient in Python, and ML frameworks/libraries.
- Experience working with: container technology, docker files, docker images, GitHub, CI/CD concepts.