Staff AI & Data Engineer

About the role

Want to be a part of Asia Pacific & Middle East's (APME) largest, most innovative, and rapidly growing data centre company? AirTrunk is a technology company with a powerful purpose - to scale and sustain the relentless growth of the region's digital future. We do this by continuously redefining and delivering hyperscale data centres that meet the needs of our customers - the world's most transformational companies. And we're doing so sustainably, for today and tomorrow. Having opened Australia's first and largest hyperscale data centres in 2017, we set our eyes on rapid expansion and now operate a platform of hyperscale data centres across the APME region. With backing from our investors, including Blackstone, this is just the beginning... Come join the A-Team at AirTrunk, where the cloud meets the ground. A Snapshot As a Staff AI & Data Engineer at AirTrunk, you will lead the design, build, and evolution of the intelligent data and AI systems that power our next-generation platforms. You'll operate at the intersection of data engineering, AI engineering, platform architecture, and DevOps, shaping scalable, secure, and production-grade solutions that drive operational efficiency, sustainability, and intelligent decision-making across our data centre and business ecosystem. This role is ideal for a highly experienced engineer who combines deep technical expertise with strong architectural judgement and a passion for building modern data and AI capabilities at enterprise scale. You will help define the technical direction, engineering standards, and platform patterns that underpin AirTrunk's AI and Data Platform, while mentoring others and driving delivery across complex, cross-functional initiatives. Your Day to Day AI Engineering & Applied Intelligence Lead the design and delivery of scalable AI and ML solutions that enhance AirTrunk's operations, from predictive maintenance and optimisation to intelligent automation and decision support. Architect and guide the development of agentic AI systems, including multi-agent patterns, retrieval-augmented generation (RAG), and Model Context Protocol (MCP) implementations that enable adaptive, enterprise-ready solutions. Drive the integration of LLM and generative AI technologies into enterprise platforms, workflows, and data products using tools such as Databricks, OpenAI, Anthropic, and LangChain. Establish reusable patterns, guardrails, and engineering standards for AI applications to ensure reliability, security, scalability, and maintainability in production. Data Engineering & Platform Foundations Lead the design and optimisation of robust batch and streaming data pipelines that support analytics, operational systems, and AI workloads at scale. Own the medallion architecture standards within Databricks Unity Catalog, bronze through semantic layers, ensuring trusted, governed, and reusable datasets. Shape scalable data architectures across the AI & Data Platform, including lakehouse, medallion, event-driven, and real-time processing patterns where appropriate. Drive improvements in data quality, lineage, observability, discoverability, and platform reliability through strong engineering practices and technical leadership. Platform Architecture, MLOps & DataOps Define and evolve platform architecture for data and AI workloads across Azure, Databricks, and related technologies, ensuring alignment with enterprise standards and long-term platform strategy. Lead the implementation of end-to-end MLOps and DataOps capabilities, including CI/CD, testing, deployment, monitoring, orchestration, and lifecycle management. Standardise tooling, infrastructure patterns, and automation approaches using Git, Terraform, Docker, Kubernetes, and related platform services. Ensure robust observability, validation, resilience, and operational support models are in place across data pipelines, models, and AI systems. Governance, Risk & Responsible Engineering Embed Responsible AI, security, privacy, and data governance practices into the design and operation of data and AI solutions. Partner with technology and business stakeholders to implement risk-aware engineering controls, compliance requirements, and trustworthy platform practices. Evaluate emerging technologies and architectural approaches with a pragmatic lens, balancing innovation, delivery value, and operational sustainability. Drive the adoption of engineering disciplines that improve platform trust, transparency, and production readiness across the AI & Data estate. Technical Leadership & Cross-Functional Delivery Provide technical leadership across complex initiatives, influencing architecture, design decisions, delivery approaches, and engineering quality across multiple teams or domains. Collaborate closely with AI, Data, Platform, Product, and business stakeholders to translate strategic priorities into scalable technical solutions. Mentor senior and junior engineers, raising the bar for e

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