Job Purpose
Aramco Artificial Intelligence (AI) organization is responsible for providing AI-powered solutions to various business functions within Aramco. The solutions leverage the latest technological advancements of (AI) and analytics to create business values across the value chain. Aramco.AI division is the center of excellence in Saudi Aramco for Artificial Intelligence technologies and data science.
The Machine Learning Engineer primary role is to work closely with business functions, stakeholders, and functional teams to give consultations on AI use case assessments, identify potential value from data, formulate AI ideas and conceptualize them. In addition, building, evaluating and productionalizing models as appropriate.
Responsibilities
- Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions
- Analyze data from company data sources to drive optimization and improvement of product development, and business strategies.
- Develop backend services and restful APIs to interface with machine learning models
- Use MLOPS concepts and tools to monitor, optimize, improve, deploy machine learning models
- Develop CI/CD pipelines for continuous integration and development of machine learning models
- Develop custom data models and algorithms as needed and appropriate to address problems at hand
- Develop A/B testing mechanisms and test model quality and value, and validate hypothesis accordingly.
- Coordinate with different functional teams to implement models and monitor outcomes.
- Develop necessary documentation as per established standards.
Education & Experience Requirements
As a successful candidate you will:
- Hold a Master’s degree in Machine Learning, Data Science, Computer Science, Computer Vision, Applied mathematics or a related field from a recognized and approved program. A PhD degree is preferred.
- Have at least 5 years of experience in building AI-based product.
- You must be fluent in Python and Machine Learning tools and libraries
- You are able to apply transfer learning on models.
- You are able to build software development, backend, services, APIs expertise.
- Proficiency in deep learning tools Keras, TensorFlow, Pytorch, Caffe, etc. is necessary
- You must also be able to bring ideas from conceptualization to productionalization (putting models in production) using the right tools.
- You are able to package and deploy front end and back end applications using docker, Kubernetes, etc.
- Strong knowledge of using data version control systems and CI/CD.
- Having good knowledge in data flow and data pipelining tools such as airflow, Kafka.
- Strong expertise in MLOPS tools such as MLflow, weights and biases, tensor board, etc.
- Proficiency in visualization tools and packages; as well as being able to communicate machine learning topics to non-technical audience is a requirement.