Machine Learning Engineer needed at Exxaro Resources

Job Description

• You will collaborate with, provide guidance and support data scientists, data engineers, and IM teams across the business on MLOps, helping to continuously develop best practices standards for ML/AI development, deployment workflows, and architecture patterns for both cloud and on-premises solutions.
• Your efforts will ensure that production model solutions are reliable, scalable, and deliver tangible business value.
• Please refer to the advert for additional in-depth details on the core deliverables of the role.

Minimum Requirements    
QUALIFICATIONS:

• BSc Information Technology (Essential/Minimum or)
• BSc Computer Science (Essential/Minimum or)
• BEng Engineering (Relevant) (Essential/Minimum or)
• BSc Engineering (Essential/Minimum)

EXPERIENCE: 

• 3-5 years Solid knowledge of core concepts/ fundamentals in one or more related fields of the following fields: AI, machine / deep learning algorithms and probabilistic methods.
• Proven experience in designing, building, and deploying ML/AI models and analytics automation processes.
• Proven experience in cloud-native development using platforms like AWS, Google Cloud, or Azure for developing and deploying machine learning models, with preference on Azure.
• Solid knowledge and experience in systems design and software architecture; machine learning frameworks and libraries such as TensorFlow, MLFlow, Keras, PyTorch and Scikit-learn. Experience in cloud-native development, including DevOps, continuous delivery, microservices, and containers.
• Cloud certification will be advantageous. Extensive experience in applying principles, theories, concepts, and techniques in programming languages with good experience in developing production-quality Python modules / frameworks being preferred. Experience in infrastructure as code such as Terraform, Ansible, ARM or other relevant IaC frameworks; Experience in version control practices using the likes of Git.
• Solid understanding of Agile development methodologies.
• Proficient in web technologies, including web services (REST) and web servers, modern HTML/CSS, and familiarity with at least one of the following (or related) programming languages: PHP, ASP.NET, or JavaScript. Strong understanding of how web applications work, including security, session management, and best development practices.
• Good knowledge of relational databases for data storage and retrieval. Experience with deployment of large language model technologies will be advantageous.
• Extensive experience in applying (Essential/Minimum) Operational

REQUIREMENTS: 

• Psychometric Assessment (Essential/Minimum) (To be conducted by Exxaro)
• Certificate of Fitness (Essential/Minimum) (To be conducted by Exxaro)

Duties & Responsibilities    
Ideal Candidate Profile (Includes but not limited to):

Ability to:

• Apply best software engineering practices to design, develop and manage robust, scalable, and maintainable machine learning and AI code and systems. 
• Development and maintenance of pipelines to support ML/AI workflows that scale development to production. 
• Automate, test, and refactor data science model code into functional ML products consumable by the business. 
• Collaborate with data scientists, data engineers/architects, and solution architects to integrate machine learning models into production systems following best practice deployment strategies suitable for various modelling use-cases. 
• Provide technical guidance to data scientists and architects on various deployment (cloud and on-prem) strategies for machine learning and AI models. 
• Continuously monitor the performance of deployed models to ensure accuracy and reliability. 
• Identify improvement areas in existing machine learning systems and implement enhancements to optimize ML systems functionality. 
• Conduct research to stay updated with the latest advancements in machine learning and artificial intelligence systems and prototype new approaches for machine learning systems. 
• Contribute to the development of best practices and standards for ML/AI end-to-end development and deployment architecture.