Machine Learning Engineer / Applied AI Engineer
Company Description We build advanced, real-time AI systems across sports analytics, financial trading, and automated decision platforms. Our projects involve large-scale data pipelines, predictive modelling, multi-agent AI architectures, and deployment of production-grade ML systems. We're looking for someone who can operate fast, solve complex technical problems, and ship high-impact AI solutions. Role Description You will work on:Building ML pipelines for sports prediction, financial data, and real-time analytics Developing advanced models (XGBoost, Light GBM, Cat Boost, Deep Learning, Ensembles) Implementing feature engineering, data cleaning, and data validation at scale Designing and maintaining real-time ingestion systems (APIs, cron jobs, streaming) Creating automated training, evaluation, and deployment workflows Integrating models with backend services, dashboards, and AI agents Running experiments, backtests, optimisations, and accuracy improvements Working directly with the founder to ship fast, iterate fast, and solve hard problems Contributing to system architecture, documentation, and long-term scalability This is NOT a “research only” role. This is a build, ship, and improve every day role. Qualifications You must be able to actually build real systems — not just talk theory. Required:Strong experience with Python and ML frameworks (scikit-learn, XGBoost, Light GBM, Tensor Flow or PyTorch) Proven experience building end-to-end ML pipelines Strong understanding of data engineering: cleaning, merging, validating, feature engineering Experience working with real APIs (REST, Webhooks, Streaming) Ability to optimise ML models and reduce inference failures Experience with Pandas, Num Py, SQL, Duck DB, or Postgres Strong debugging skills and ability to work independently Experience with version control (Git) and production code standards Ability to handle large datasets (GB-scale) Bonus (highly preferred):Experience in sports analytics (NRL, NBA, MLB, EPL, AFL, horse racing, etc.) Experience with time-series modeling and walk-forward validation Experience deploying ML in cloud or container environments Experience with multi-agent AI systems (Lang Chain, ReAct, tool-using models) Understanding of trading, probabilities, CLV, or risk modelling Ability to create clear documentation and structured code What You Will Work On Building a real-time prediction engine for a conglomerate of sports Engineering hundreds of custom features from raw JSON/CSV data Creating automated model-training pipelines Optimising data ingestion speed and model latency Writing high-quality, production ML code used daily Helping scale multiple AI systems across different domains Who We’re Looking For Someone who:Doesn’t need hand-holding Loves solving difficult problems Writes clean, tested, maintainable code Has a builder mentality Wants to work on ambitious AI projects, not boring corporate tasks If you are slow, sloppy, or passive — you will drown here. If you are sharp, proactive, and ambitious — you’ll thrive. Salary & Benefits Competitive compensation based on skill level and delivery speed. Opportunity to grow into lead engineer across multiple AI verticals and partner in the business.