Principal Engineer - Data & AI

Preacta; Sydney; On-site; AUD 180,000 - 280,000; Full time

Job summary

Preacta, a leading financial services organisation in Australia, is seeking a senior ML/engineering leader to direct data and AI platform development. You will set technical direction for data usage, with ML and decisioning as priorities.

You will design real-time and batch decisioning from customer signals, build the MLOps environment, and empower business teams with self-serve data pipelines and contracts.

Qualifications

• Senior, hands-on engineering role responsible for data and AI platform direction.
• Focus on real-time and batch decisioning using customer signals.
• Define governance, access control, and regulatory requirements within the platform.
• Shape self-serve data pipelines and data product sharing with contracts.
• Explore agentic workflows and AI-native data engineering.
• Coordinate with partner teams and vendors; influence architecture decisions.
• Mentor engineers and lead complex programs.

Responsibilities

• Design real-time and batch decisioning to turn signals into actions.
• Build and maintain the MLOps environment.
• Shape self-serve data pipelines and publish data products.
• Establish engineering standards across batch/real-time/graphs/unstructured data.
• Embed governance and regulatory requirements into the platform.
• Drive DevOps/SRE practices in shipping.
• Lead with platform engineering lead on roadmaps; coach engineers.
• Explore agentic AI workflows to create datasets.

Skills

ML engineering

Data engineering

Vendor influence

Regulated industry

Senior level

Real-time decisioning

Agentic AI

Job description

A large, well-known financial services organisation, regulated and complex, with a big gap it wants to close in machine learning and decisioning. They're building out the capability to act on customer signals in real time and at scale, alongside an external technology partner and a team of in-house engineers. The structure is still taking shape, so there's room to influence how it's built.

Role and responsibilities

This is a senior, hands-on engineering role at principal level, sitting across the data and AI platform. You'll set technical direction for how data is built onto it and used from it, with ML and decisioning as the first priority.

Day to day, that means designing real-time and batch decisioning that turns customer signals into personalised action, and building the MLOps environment that supports it. You'll also help shape the self-serve side: how business teams build their own pipelines, publish data products and share data under clear contracts. A citizen ML application is part of the picture too.

You'll set the engineering standards and patterns that others build to, covering batch, real-time, graph and unstructured data. You'll build governance, access control and regulatory requirements into the platform itself, and keep an eye on performance, cost and reliability. You'll also bring DevOps and SRE habits into the way the team ships.

There's an agentic angle as well. You'll build workflows where AI agents help create datasets, a step towards an AI-native approach to data engineering.

Much of the engineering capacity sits with a partner, so influence is a big part of the job. You'll challenge architecture and stack decisions, including with large vendors, and work with the platform engineering lead and product on the roadmap. You'll act as a technical lead on complex programs and coach engineers along the way.

Key technical skills

• Depth in ML engineering and decisioning
• Strong data engineering fundamentals and cloud data platform experience
• Proven influence with vendors and partners, including pushing back on big ones
• Regulated industry experience and the ability to navigate a large organisation
• Hands-on credibility at a genuinely senior level
• Personalisation, next best action or real-time decisioning in financial services or similar
• AI-native mindset and experience with agentic architectures