Developers/Programmers (Information & Communication Technology)
Full time
Santos provides reliable, affordable energy for progress and seeks to provide lower carbon energy over time.
Santos is a global energy company with operations across Australia, Papua New Guinea, Timor-Leste and the United States.
At Santos, our goal is to be a global leader in the energy evolution to low-carbon fuels that help the world decarbonise and continue to provide the reliable, affordable energy the world needs for modern life and human progress.
About the Role
Based at our global headquarters in Adelaide, we are seeking a skilled Machine Learning Operations Developer to join our team and play a key role in streamlining the deployment, monitoring, and management of machine learning (ML) models across the organisation.
In this role, you will work at the intersection of data science, development, and operations to ensure that ML models are deployed efficiently, maintained with high reliability, and continuously optimized for performance.
You will be responsible for automating and optimizing the ML lifecycle from data pipelines and model training to deployment and monitoring, ensuring the scalability, availability, and performance of ML systems in production environments.
Key accountabilities include:
Develop and maintain the infrastructure needed to support the development, deployment, and scalability of machine learning models in production environments.
Automate the entire ML workflow, including model training, testing, deployment, monitoring, and versioning, to streamline operations and reduce manual intervention.
Continuously monitor the performance of ML models in production, ensuring they meet accuracy, efficiency, and scalability requirements.
Work closely with data scientists and engineers to implement best practices for model versioning, reproducibility, and automation strategies.
Design and implement efficient data pipelines that support data preprocessing, feature engineering, and model training processes.
Ensure proper governance of ML models by tracking, versioning, and logging model changes for compliance, auditing, and reproducibility.
Continuously optimize the performance of ML models by fine-tuning algorithms, adjusting parameters, and leveraging scalable cloud architectures.
Collaborate with cloud platform teams to manage, deploy, and scale ML models using cloud services such as AWS, Azure, or GCP.
Keep up to date with the latest MLOps tools and practices, implementing new technologies and strategies to improve automation, deployment, and monitoring processes.
About You
You will be an experienced professional with a strong background in Machine Learning Operations (MLOps), DevOps, or a related field.
You will have hands-on experience deploying and managing machine learning models in production environments, alongside a proven track record in building and maintaining CI/CD pipelines for ML models using tools such as Azure DevOps, GitLab, or similar platforms.
You will possess expertise in developing and managing data pipelines and infrastructure to support machine learning model training and deployment.
Familiarity with cloud platforms such as AWS, Azure, and GCP, and experience deploying ML models via services like SageMaker, Azure ML, or GCP AI Platform is essential.
Strong programming skills in Python and Shell scripting, along with experience in machine learning libraries like TensorFlow, PyTorch, and scikit-learn, is a must.
You should also be adept in model governance, ensuring model versioning, tracking, and reproducibility for compliance and auditing purposes.
A solid understanding of database management (SQL and NoSQL), along with experience working on data pipelines for training, inference, and real-time data processing, is key to success in this role.
Ideally, you will be degree qualified in Computer Science, Machine Learning, Data Science, or a related field.
Certifications in DevOps or cloud platforms (e.g., AWS Certified DevOps Engineer, Google Cloud Certified – Professional Data Engineer) are highly desirable.
How to Apply
Applications must be submitted via the online recruitment system before COB Monday the 23rd of December 2024.
A requirement of this role is that you will need to have in place the legal work rights for Australia to apply.
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