Data Engineer with ML Ops Expertise

1 day ago

Sydney, New South Wales, Australia N2S Full time $120,000 - $180,000 per year

Job Summary:

We are seeking a skilled
Data Engineer with ML Ops experience
to join our data and AI team. The ideal candidate will be responsible for building scalable data pipelines, managing data infrastructure, and deploying machine learning models into production environments. You will collaborate closely with data scientists, software engineers, and DevOps teams to ensure robust, automated, and efficient ML workflows.

Key Responsibilities:

Data Engineering:

  • Design, build, and maintain scalable ETL/ELT pipelines.
  • Develop and optimize data models for analytics and machine learning.
  • Work with structured and unstructured data from various sources (APIs, databases, cloud storage).
  • Ensure data quality, integrity, and governance across systems.

ML Ops:

  • Automate ML model training, validation, deployment, and monitoring.
  • Implement CI/CD pipelines for ML workflows.
  • Manage model versioning, reproducibility, and rollback strategies.
  • Monitor model performance and data drift in production.
  • Collaborate with data scientists to productionize ML models.

Infrastructure & Tools:

  • Work with cloud platforms (AWS, Azure, GCP) for data and ML workloads.
  • Use tools like Airflow, Kubeflow, MLflow, Docker, and Kubernetes.
  • Maintain and optimize data lakes, warehouses, and feature stores.

Required Qualifications:

  • Bachelor's or Master's degree in Computer Science, Data Engineering, or related field.
  • 3+ years of experience in data engineering and/or ML Ops.
  • Proficiency in Python, SQL, and cloud-native data tools.
  • Experience with ML Ops frameworks (e.g., MLflow, TFX, SageMaker).
  • Familiarity with containerization and orchestration tools (Docker, Kubernetes).
  • Strong understanding of data architecture and machine learning lifecycle.

Preferred Qualifications:

  • Experience with real-time data processing (Kafka, Spark Streaming).
  • Knowledge of data governance and compliance (GDPR, HIPAA).
  • Exposure to monitoring tools (Prometheus, Grafana).
  • Certifications in cloud platforms or ML Ops tools.

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