Data Engineer – AI
Sydney Olympic Park, New South Wales 2127, Australia
AUD 110K-140K (estimate) Mid-level Full Time
Tasks
- Build feature engineering pipelines
- Build forecasting predictive analytics clustering and recommendation solutions
- Build scalable Google Cloud data pipelines
- Conduct A B testing backtesting and production validation
- Create technical documentation architectures and operational runbooks
- Develop APIs and event driven integrations for AI predictions
- Develop data models and analytical datasets in BigQuery
- Develop train tune evaluate and deploy ML models with Vertex AI
- Implement data quality controls automated testing and model validation
- Implement retry fallback rollback and human escalation mechanisms
- Integrate AI ML solutions with enterprise ecommerce inventory and operational systems
- Monitor model performance data drift latency errors and AI costs
- Set up Google Cloud Logging Monitoring and Observability
- Support and continuously improve production AI systems
- Translate commercial problems into scalable AI solutions
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | Agile | Automated testing | B testing | Backtesting | BigQuery | Cloud Governance | Cloud Monitoring | Cloud logging | Cloud platform | Clustering | Data Modeling | Data Security | Data Validation | ELT | ETL | Embeddings | Event Driven | Event-driven architecture | Feature Engineering | Function Calling | Generative AI | Google Cloud | Google Cloud Logging | Google Cloud Monitoring | Google Cloud Platform | Hybrid search | IAM | LLM | Langchain | Langgraph | MLOps | Machine Learning | Model Evaluation | Observability | Optimization | Predictive Analytics | Python | REST API | Recommendation Systems | SQL | Scrum | Semantic Search | Series forecasting | Time Series | Time Series Forecasting | Tool-Calling | Vector Databases | Vertex AI
Education
Associate Degree | Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
AI | AI Engineer | Data Engineer | Engineer
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