Data AI/ML Engineer

1 day ago

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

We're partnering with a rapidly scaling and innovative leader in the
Digital Media and Entertainment
sector, dedicated to
optimising
user experience and content recommendation through cutting-edge Machine Learning. For the right candidate with the necessary skills and experience, we are pleased to offer
482 visa sponsorship
.

This client requires a
Data AI/ML Engineer
to bridge the gap between data science and production engineering. You will be instrumental in designing the
MLOps
platform, building robust feature pipelines, and deploying high-performance ML models (such as recommendation engines and user prediction systems) into a live, high-traffic environment. This role demands expertise in both cloud data architecture and production machine learning best
practises
.

What You'll Do

  • Design and build
    scalable, automated data pipelines (ETL/ELT) for feature engineering, training, and model serving using cloud services like AWS Glue and EMR.
  • Lead the deployment and operationalisation
    of machine learning models (MLOps) into production environments, utilizing platforms like
    AWS SageMaker
    for continuous integration and continuous delivery (CI/CD).
  • Develop and maintain feature stores
    and real-time data services to ensure low-latency model prediction serving.
  • Collaborate closely
    with data scientists to transition experimental models into resilient, production-ready code, focusing on performance, scalability, and cost
    optimisation
    .
  • Implement monitoring and alerting
    for model performance, data drift, and data quality in production.
  • Champion MLOps and DevSecOps practises
    for the ML platform, ensuring code quality, security, and reproducibility across the entire model lifecycle.
  • Contribute to architectural decisions
    for the overall data and ML infrastructure.

What You'll Bring

  • 4+ years of professional experience in Data Engineering or ML Engineering, with a proven track record of deploying models into production.
  • Expert proficiency in
    Python
    and deep experience with ML frameworks such as
    TensorFlow or PyTorch
    .
  • Mandatory hands-on experience with AWS cloud services
    for data and ML (e.g., SageMaker, EMR, S3, Lambda).
  • Strong experience with the
    MLOps lifecycle
    and tools for model management, versioning, and monitoring.
  • Expert-level SQL proficiency and solid understanding of data warehousing and data lake architectures.
  • Familiarity with containerisation (Docker) and orchestration (Kubernetes) for model deployment.
  • Excellent communication skills, with the ability to articulate complex technical requirements to data scientists and software engineers.

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