Staff AI & Data Engineer

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 engineering excellence, reusable design, and operational maturity across the team.
• Actively contribute to AirTrunk’s Data & AI community of practice by sharing knowledge, shaping standards, and building capability across the broader organisation.

Requirements  

• 10+ years’ experience in data engineering, AI/ML engineering, software engineering, or platform engineering, with a strong track record of delivering production-grade data and AI solutions in complex enterprise cloud environments.
• Demonstrated ability to architect and lead scalable, high-performance, secure, and resilient data and AI platforms that meet enterprise standards and support multiple business domains and use cases.
• Deep experience designing, building, and evolving robust batch and streaming data pipelines, event-driven architectures, and curated data models that enable analytics, operational workloads, and AI applications at scale.
• Strong hands-on expertise in modern data architecture patterns, including lakehouse and medallion architectures, real-time data processing, data product thinking, and platform-oriented engineering practices.
• Proven experience designing and deploying AI/ML solutions in production, including LLM-based applications, intelligent automation, retrieval-augmented generation (RAG), and agentic AI patterns where appropriate.
• Strong experience with Azure and Databricks preferred, alongside proficiency with modern data and integration technologies such as dbt, Kafka, Azure Event Hubs, orchestration frameworks, and associated cloud-native services.
• Advanced programming capability in Python and SQL, with practical experience in software engineering disciplines such as testing, code quality, version control, and automation. Experience with JavaScript or Shell is beneficial.
• Deep understanding of MLOps and DataOps practices, including CI/CD, model and pipeline lifecycle management, monitoring, observability, validation, containerisation (Docker/Kubernetes), and infrastructure as code (Terraform).
• Experience establishing technical standards, reusable patterns, and platform guardrails that improve engineering consistency, delivery quality, and operational supportability across teams.
• Strong understanding of data governance, privacy, security, and Responsible AI principles, with the ability to embed these into architecture, platform design, and engineering workflows.
• Demonstrated ability to lead complex, cross-functional technical initiatives and influence architecture, delivery, and engineering decisions across multiple teams, domains, or platforms.
• Strong stakeholder engagement and communication skills, with the ability to translate strategic objectives into practical technical direction and execution.
• Proven capability to mentor engineers, uplift technical capability, and drive a culture of engineering excellence, pragmatism, and continuous improvement.