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.