Machine Learning Engineer

Key Skills

Java Programming, Python Programming, SQL, Apache Kafka, Machine learning techniques, Deep Learning, Keras software library, PyTorch, TensorFlow, Docker, Kubernetes-K8s, Continuous Integration & Continuous Delivery - CI/CD, MLflow, Streaming APIs, MLOps tools, Data pipelines

Job Description

Aramco occupies a unique position in the global energy industry. We are the world's largest producer of hydrocarbons (oil and gas), with the lowest upstream carbon intensity of any major producer.

With our significant investment in technology and infrastructure, we strive to maximize the value of the energy we produce for the world along with a commitment to enhance Aramco’s value to society.

Headquartered in the Kingdom of Saudi Arabia, and with offices around the world, we combine market discipline with a generations’ spanning view of the future, born of our nine decades experience as responsible stewards of the Kingdom’s vast hydrocarbon resources. This responsibility has driven us to deliver significant societal and economic benefits to not just the Kingdom, but also to a vast number of communities, economies, and countries that rely on the vital and reliable energy that we supply.

We are one of the most profitable companies in the world, as well as amongst the top five global companies by market capitalization.

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

You will be required to perform the following:

• 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.