MLOps Engineer

vor 4 Wochen

City of Brisbane, Österreich PRA Vollzeit

This range is provided by PRA. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more. Work with a country wide recognised brand growing in the AI space Our client is a leading Australian organisation investing heavily in advanced data, AI and machine learning capabilities to support large‑scale, mission‑critical operations. An opportunity now exists for an experienced MLOps Engineer to play a key role in taking machine learning models from prototype through to reliable, cost‑effective production. The Role As an MLOps Engineer, you will be responsible for the full lifecycle of ML/AI solutions in production. You will work closely with Data Scientists, Data Engineers, DevOps and application teams to design, deploy and operate scalable machine learning systems in a cloud environment. Key responsibilities include: Transitioning machine learning models from Databricks notebooks into production using best‑practice ML and pipeline patterns Designing, building and maintaining end‑to‑end ML and data pipelines Maintaining and optimising Databricks infrastructure (compute, catalogues, users, upgrades) Monitoring performance, cost, stability and operational efficiency of ML platforms Writing and reviewing technical documentation, designs and code About You You are a hands‑on MLOps or DevOps engineer with a strong software engineering mindset and experience supporting machine learning solutions at scale in cloud production environments. Essential experience and skills: 3+ years’ experience in DevOps, MLOps or software engineering roles in a cloud environment Strong experience with AWS and cloud‑native architectures Proven experience deploying and supporting ML models in production Advanced Python and Spark experience Strong understanding of ML model lifecycle and operationalisation CI/CD pipeline automation experience (e.g. Azure DevOps or similar) Infrastructure‑as‑code experience (Terraform) Databricks platform experience (including MLflow and Feature Stores) Experience with relational and non‑relational data stores Exposure to large‑scale or shared data platforms AWS or Databricks certifications Qualifications Degree in Computer Science, Engineering, IT or a related quantitative discipline Cloud certifications highly regarded (AWS, Databricks) If you’re passionate about turning data science into reliable, scalable production systems and want to work on complex, meaningful problems, we’d love to hear from you. Seniority level Mid‑Senior level Employment type Contract Job function Information Technology Industries Oil and Gas #J-18808-Ljbffr

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