Principal AI Engineer - Technical Lead

• Own the technical architecture and end-to-end delivery of each workstream, from data foundations through models and optimisation to evidence proving effectiveness
• Serve as the technical face of the engagement
• Run methodology reviews, steering sessions, and executive readouts for client leaders and partners
• Lead a pod of machine learning and data engineers
• Set standards for code review, testing, reproducibility, and documentation
• Build and operate production data pipelines on Google Cloud Platform and BigQuery at large scale across billions of rows
• Apply cost discipline and data governance controls
• Design, ship, and validate propensity, uplift, visitation frequency, and offer adoption models
• Apply causal inference in production, including uplift modelling, matched control difference in differences, double machine learning, holdout and experiment design, and power analysis
• Build optimisation engines using mixed integer and linear programming, assignment methods, and scheduling methods
• Maintain architecture documents, decision registers, and evidence packs suitable for adversarial technical review
• Translate technical work into plain language for business and executive audiences
• Collaborate with client business and technical teams and advisory and consulting partners

Requirements

• Ten or more years building and shipping production data and machine learning systems, including time spent leading technically or leading a team
• Deep, hands on Python and SQL
• Production data engineering on a major cloud platform
• Applied causal inference and experimentation, including uplift modelling, difference in differences, double machine learning, and power analysis
• Mathematical optimisation applied to operational problems, including mixed integer or linear programming and assignment or scheduling problems
• Track record of client facing delivery and presenting to senior executives and external delivery partners
• Experience building agentic workflows and integrating LLMs and agentic systems into production solutions
• Production grade testing, reproducibility, data lineage, and documentation
• Comfort operating in regulated, high governance data environments
• Right to work in Australia, or a base in a time zone within about two hours of Australian Eastern Time
• Resume and application materials must be submitted in English
• Melbourne preferred; strong candidates elsewhere in Australia considered
• Experience with Google Cloud Platform and BigQuery ideal; AWS, Databricks, or Microsoft Azure experience transfers well
• Domain experience in gaming, casino, hospitality, or loyalty and marketing analytics is a nice to have
• Experience with recommendation systems, marketing and offer optimisation, or workforce and roster optimisation is a nice to have
• Experience integrating vendor systems for yield management, workforce management, or player and table tracking is a nice to have
• Background in responsible gaming, anti money laundering, or privacy by design is a nice to have
• Consulting or client embedded delivery experience is a nice to have

Core Competencies

Demonstrates expertise in building and delivering production data and machine learning systems, with a strong focus on applied causal inference, mathematical optimization, and client facing delivery. Proficient in managing technical architecture and collaborating with cross functional teams in regulated data environments.

Highest signal resume keywords

• Python
• SQL
• Google Cloud Platform
• BigQuery
• Causal Inference

Hard Skills

• Machine Learning
• Data Engineering
• Mathematical Optimization
• Production Data Pipelines
• Uplift Modelling
• Mixed Integer Programming
• Linear Programming
• Testing and Reproducibility
• Data Governance
• Documentation

Soft Skills

• Client Facing Delivery
• Communication
• Leadership

Industry Keywords

• Gaming
• Casino
• Hospitality
• Loyalty Analytics
• Marketing Analytics

Tools & Technologies

• AWS
• Databricks
• Microsoft Azure