Reapit – Who are we?
Reapit is the original, end-to-end business technology provider for estate agencies of all sizes. We’ve been helping sales and lettings agents to build relationships and grow their businesses for more than 25 years. Our technology connects property professionals in Europe, the Middle East, Australia, and New Zealand with buyers, sellers, tenants and landlords to power the relationships that change lives.
In Australia, Reapit stands as the preferred technology choice among the nation's leading estate agents and agencies. Tailored to the unique demands of the Australian property market, Reapit provides successful leaders with unparalleled tools across sales, property management, client relations, and data analytics, reinforcing their position at the pinnacle of real estate excellence.
What you’ll be doing
Reapit is investing in applied AI that turns customer data into predictive intelligence, embedded directly in users' daily operations where it supports better, faster decisions.
This means bringing the right data together, building and deploying predictive models that are explainable and improve over time, and surfacing their outputs where they're genuinely useful to users.
As Senior AI Engineer, you will own the technical path from data foundations to production intelligence: resolving customer identity, engineering signals and features, building predictive models, instrumenting feedback loops, and surfacing recommendations where they change user behaviour.
This is a hands-on, delivery-focused contract with high visibility across Product, Architecture, Security, DevOps and Commercial teams, focused on shipping the first production capability while leaving clean, well-documented foundations for future AI phases.
Key Responsibilities
Predictive Modelling (core)
Design, build, evaluate and ship predictive models that rank contacts by likelihood of a defined outcome, including calibrated scores, contributing signals and clear explanations.
Frame each model around the business decision it supports, including prioritisation, timing, personalisation or automation, and define how success will be measured in production.
Identity Resolution & Unified Customer Record
Build the matching capability that unifies customer records across applications, combining deterministic rules with probabilistic record linkage and explainable match decisions.
Establish the core customer-data model, including identity graph, match and merge decisions, audit trail, synchronisation rules and conflict handling.
Signals, Feature Store & Data Engineering
Build the layer that captures, stores and serves the behavioural and contextual signals used for modelling, including a feature store for consistent training and serving.
Profile and validate source data, establishing what exists today, where it lives, how it is captured, and how reliable or noisy it is, and design pipelines that cope with inconsistent, user-entered data.
Activation & Closed-Loop Learning
Embed model outputs into user workflows through ranked lists, prioritisation feeds, alerts and triggers that guide action rather than simply report insight.
Capture user actions and downstream outcomes as feedback, turning real-world results into labels that improve model performance over time.
Productionisation & MLOps
Deploy models as reliable, cost-efficient services on AWS, integrated with existing systems and third-party APIs while maintaining data consistency.
Implement monitoring, logging and observability for model performance and data drift.
Establish evaluation and A/B testing so model and workflow changes are measured against real business outcomes.
Automation, Orchestration & Future Phases
Lay the technical foundations for later phases, including communication and sentiment understanding, engagement automation, retention intelligence and agent-driven workflows
Design extensible patterns that allow future AI capabilities to build on the same customer, signal and feedback infrastructure without rework.
Who we're looking for
At Reapit, we prioritise hiring individuals who share our values and possess the right attitudes and behaviours for success. Whilst some of the listed requirements may be important, don’t worry if you don’t meet all of them, we’d still like to hear from you.
5+ years of software/data engineering experience, including 3+ years building and shipping ML systems to production in enterprise or SaaS environments.
Demonstrable experience delivering predictive scoring models end-to-end, such as propensity, churn, lead-scoring or similar models, including feature engineering on behavioural/event data, probability calibration, and rigorous evaluation (precision/recall, ROC-AUC, lift).
Experience designing feedback or closed-loop ML systems: instrumenting outcome capture, generating labels from real-world results, and improving models over time.
Strong Python for data science and ML, fluent with the modern tabular stack (e.g. scikit-learn, XGBoost/LightGBM) and feature engineering; experience building or using a feature store.
Hands-on experience with entity resolution / record linkage / fuzzy matching, ideally including explainability of match decisions.
Solid data engineering: building pipelines, handling messy and user-entered data, data quality and validation; strong SQL and experience with MySQL.
Production AWS experience (e.g. Lambda, ECS/EKS, S3, CloudFormation; SageMaker and/or Bedrock a plus).
MLOps fundamentals: deployment, monitoring, drift detection, and A/B testing or evaluation frameworks.
Sound software architecture, API design and system-integration skills.
Able to operate independently and deliver within a fixed contract timeframe with minimal ramp-up.
Comfortable bringing solutions to market via customer-facing delivery into our tech stack, with full-stack engineering experience (React, .Net/Java/OO, AWS)
What your impact and success looks like
We expect your success and impact in the early stages of your career with us to look something like this:
Within 1 month:
You’ll be discovering how to, and actively integrate AI functionality into simple features in our products
Participating with the wider team, determining AI strategy and tooling
Within 3 months:
You will feel comfortable with the basics of the domain and Reapit’s products & services.
You will be able to demonstrate an understanding of the core areas of the technology stack.
You will be independently contributing new ideas to product and processes.
You will be contributing more advanced agentic features to Reapit products, and helping hone AI strategy from initial feature feedback
Within 6 months:
You will be able to work independently due to your continually growing understanding of our products and solutions.
Your contributions will be helping to shape the backlog and future direction of the wider products, where AI exists at its core.
You’ll be an advocate for the AI strategy throughout the business, guiding the wider teams adoption
We operate a Flexible Working Policy and we would like for you to work from our Sydney or Brisbane office, occasionally making an in-person appearance but otherwise able to work-from-home.
Don't tick all the boxes? Neither do we
We care about our industry and want it to become a more inclusive and diverse place to work. So, we’re driven by hiring not only by experience and relevance for the role but by sharing our values and the right attitudes and behaviours for success. We are committed to Equal Employment Opportunity through attracting and retaining a complementary team of employees and building an inclusive environment for all. We feel we have an empowering environment where everyone is supported and respected, and we want you to feel this too. We welcome new ideas, thinking and approaches, whilst listening to all
our employees.
“We are a 2025 Circle Back Initiative Employer – we commit to respond to every applicant.”
