Location:Ref#:484196Description & RequirementsAbout UsSantos 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.
Our goal is to be a global leader in the energy evolution to low-carbon fuels that help the world decarbonise while providing the reliable, affordable energy the world needs for modern life and human progress.Santos is an important Australian domestic gas supplier and LNG supplier in Asia.
We are committed to supplying critical fuels such as oil and gas, and abating emissions through carbon capture and storage, energy efficiency projects, use of renewables in our operations, and high-quality offsets.
We also seek to develop low-carbon fuels as customer demand evolves.For 70 years, Santos has been working in partnership with local communities, providing jobs and business opportunities, and safely developing natural gas resources to power industries and households.The Santos portfolio is value accretive and resilient across a range of decarbonisation scenarios.
We have a climate transition action plan that will continue to evolve for the global energy evolution.About the RoleBased at our global headquarters in Adelaide, we are seeking a skilled Machine Learning Operations Developer to join our team.
In this role, you will work at the intersection of data science, development, and operations to ensure that ML models are deployed efficiently and maintained with high reliability.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.
This is critical for enabling data-driven decision-making across the organisation.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.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 and reproducibility.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.Continuously optimize the performance of ML models by fine-tuning algorithms 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.About YouYou 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 should possess expertise in developing and managing data pipelines and infrastructure to support machine learning model training and deployment, as well as familiarity with cloud platforms such as AWS, Azure, and GCP.
Strong programming skills in Python and Shell scripting, along with experience in machine learning libraries like TensorFlow, PyTorch, and scikit-learn, are essential.A solid understanding of database management (SQL and NoSQL) and experience working on data pipelines for training, inference, and real-time data processing are 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.Supporting a Diverse WorkforceSantos acknowledges that we operate on the traditional lands of Aboriginal people.
We are committed to an inclusive environment where people feel comfortable to be themselves.
We treat each other fairly and with dignity regardless of race, gender, nationality, ethnic origin, religion, age, sexual orientation, or anything else that makes us different.Australian Aboriginal and/or Torres Strait Islander jobseekers are strongly encouraged to apply.How to ApplyApplications must be submitted via the online recruitment system before COB Monday the 6th of January 2025.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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