4 days ago
Sydney, Australia or Melbourne, AustraliaStaff+
Responsibilities
- Lead the design and delivery of scalable AI and ML solutions for predictive maintenance, optimisation, automation, and decision support.
- Architect agentic AI systems using multi-agent patterns, retrieval-augmented generation, and Model Context Protocol implementations.
- Integrate LLM and generative AI technologies into enterprise platforms, workflows, and data products.
- Design and optimise batch and streaming data pipelines, lakehouse architectures, medallion architectures, and real-time processing systems.
- Define platform architecture and lead MLOps, DataOps, deployment, monitoring, orchestration, and lifecycle management capabilities.
- Standardise tooling, infrastructure patterns, automation, observability, validation, resilience, and operational support models.
- Embed responsible AI, security, privacy, data governance, compliance, and risk-aware engineering controls into platform design.
- Provide technical leadership across complex initiatives, influence architecture and engineering decisions, and mentor engineers across teams.
Requirements
- 10+ years of experience in data engineering, AI/ML engineering, software engineering, or platform engineering.
- Experience delivering production-grade data and AI solutions in complex enterprise cloud environments.
- Ability to architect scalable, secure, resilient, and high-performance data and AI platforms supporting multiple domains and use cases.
- Deep experience with batch and streaming pipelines, event-driven architectures, curated data models, lakehouse and medallion architectures, and real-time processing.
- Production experience with AI/ML solutions, LLM-based applications, intelligent automation, retrieval-augmented generation, and agentic AI patterns.
- Strong experience with Azure and Databricks, with proficiency in technologies such as dbt, Kafka, Azure Event Hubs, and orchestration frameworks.
- Advanced programming capability in Python and SQL; JavaScript or Shell experience is beneficial.
- Experience with MLOps, DataOps, CI/CD, lifecycle management, monitoring, observability, validation, Docker, Kubernetes, and Terraform.
- Experience establishing technical standards, reusable patterns, platform guardrails, governance practices, and operational support models.
- Strong understanding of data governance, privacy, security, and Responsible AI principles.
- Demonstrated ability to lead cross-functional technical initiatives, influence stakeholders, and mentor engineers.
Benefits
- Flexible and safe working environment with a culture focused on professional growth through Grow@Hyperscale.
Tech Stack
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