Machine Learning OPS engineer

Position: Machine Learning OPS engineer

Location: Sydney

Duration: 6 months

We are seeking a highly skilled and motivated Machine Learning Engineer with deep expertise in statistical modeling, experimental design, and modern ML development practices. The ideal candidate will have hands-on experience in building, deploying, and maintaining scalable ML solutions using cloud platforms and MLOps frameworks.

Key Responsibilities:

• Design, develop, and deploy machine learning models using Python and modern ML frameworks.
• Apply statistical modeling and experimental design to solve complex business problems.
• Work with cloud platforms such as AWS SageMaker, Google Vertex AI, and Azure ML for scalable model training and deployment.
• Implement MLOps best practices including CI/CD pipelines for ML, model versioning, monitoring, and automated retraining.
• Handle large-scale data processing using distributed computing frameworks like Apache Spark, Ray, and Dask.
• Perform advanced feature engineering and ensure model interpretability, fairness, and compliance with responsible AI principles.
• Translate complex ML solutions into actionable business insights and communicate findings effectively to both technical and non-technical stakeholders

Deploy and Monitor ML Models – Ensure models are successfully deployed into production environments and continuously monitor their performance and reliability.

Automate ML Pipelines – Build and maintain automated workflows for data processing, model training, testing, and deployment using CI/CD practices.

Manage Model Lifecycle – Track model performance, detect drift, retrain models when necessary, and optimize infrastructure for scalability and cost efficiency.

Must Have Skills:

• Strong proficiency in Python and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Experience with cloud-based ML platforms (AWS, GCP, Azure).
• Solid understanding of MLOps tools and practices (CI/CD, model monitoring, retraining).
• Familiarity with distributed computing and big data tools (Spark, Ray, Dask).
• Knowledge of responsible AI principles including fairness, transparency, and accountability.
• Excellent communication skills and ability to present technical concepts to diverse audiences.

Nice to Have Skills: Prior experience in translating ML solutions into measurable business impact