AI & Data Engineer

Job Description

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.

Are you interested in this position?
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