Responsibilities
Collaborate with data scientists and software engineers to design and implement machine learning workflows.
Take offline models data scientists build and turn them into a real machine learning production system.
Develop and deploy scalable custom tools and services that can handle machine learning training and inference
Apply software engineering best practices to machine learning such as CI/CD, versioning and Containerization
Develop machine learning algorithms and libraries for problem solving and AI operations.
Research and provide input on design approach, performance and base functionality improvements for various software applications.
Stay up to date with the latest developments in machine learning and cloud computing technologies.
Qualifications
BS or MS in computer science or equivalent practical experience
At least 2-3 years of coding experience in a non-university setting.
Proven experience in Object Oriented development
Experience in deploying and managing Machine Learning models at scale
Experience with MLOps platforms such as Kubeflow, MLFlow, Sagemaker etc.
Familiarity with DevOps practices and tools such as Kubernetes, Docker, Jenkins, Git.
Proficient understanding of distributed computing principles
Experience with NoSQL databases, such as HBase, Cassandra, MongoDB
Demonstrated proficiency with data structures, algorithms, distributed computing, and ETL systems.
Good knowledge of and experience with big data frameworks such as Apache Hive, Spark
Strong understanding of machine learning concepts and frameworks, including TensorFlow, PyTorch, Scikit-learn, Kedro etc.
Apply via :
egjd.fa.us6.oraclecloud.com
