Principal Ai Engineer - Technical Lead

Jobtailor; City of Melbourne; On-site; AUD 180,000 - 260,000; Full time

Job summary

Jobtailor is seeking an experienced leader to own architecture and end-to-end delivery of ML and data engineering workstreams on a Melbourne-based project. You will steer methodology reviews, run executive readouts, and manage a pod of engineers across data foundations, models, and optimisation.

The role emphasizes hands-on Python/SQL, production data pipelines on cloud platforms, and expert communication with client leaders in regulated environments.

Qualifications

• Proven hands-on Python and SQL in production systems.
• Experience with cloud data platforms and data pipelines at scale.
• Applied causal inference and experimentation in production environments.

Responsibilities

• Own the technical architecture and end-to-end delivery of each workstream.
• Lead a pod of ML and data engineers and set coding/testing standards.
• Translate complex technical work into business-friendly language for executives.

Skills

Python

SQL

Google Cloud Platform

BigQuery

Causal Inference

Production Data Pipelines

Tools

AWS

Databricks

Microsoft Azure

Job description

Job Description

• 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