Entain
Information Technology and Services
MachineLearningEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Machine Learning Engineer at Entain. Skills: Machine Learning, MLOps, Model deployment. Monitor live models. Improve live models”
What You'll Achieve.
Build scalable solutions; Influence customer experience; Influence business performance; Deliver real impact
Industry & Context.
What They're Looking For.
Must Have
Experience deploying machine learning models, Python skills, Experience with AWS, Experience with large datasets, Experience with production systems
Nice to Have
Experience in personalisation, Experience in recommendation systems, Experience in generative AI
What You'll Do.
Analyse model performance
Analyse model outcomes
Partner with product teams
Partner with engineering teams
Identify ML opportunities
Build feature pipelines
Deploy feature pipelines
Build inference services
Deploy inference services
Strengthen MLOps practices
Improve deployment speed
Improve deployment reliability
Improve deployment observability
Collaborate with data scientists
Collaborate with engineers
Take ideas to production
Contribute to architecture decisions
Raise ML engineering standards
How You'll Work.
Team & Collaboration
Product teams; Engineering teams; Data scientists; Engineers
Communication Scope
Influence stakeholders
Full Job Description
**Who We Are** Entain Australia & New Zealand is fearlessly transforming the racing, sports, and entertainment landscape - reimagining how customers experience their favourite brands. As part of a global powerhouse operating in over 40 countries with nearly 30,000 people, we’re home to leading ANZ names like Ladbrokes, Neds, TAB, and Betcha. Across Entain ANZ, we’re shaping the future of sport and gaming entertainment through creative innovation, storytelling, and technology - creating experiences that connect with millions of fans. **Your Impact** This is where AI meets real-world scale. As a Machine Learning Engineer at Entain, you’ll build and deploy intelligent systems that power personalisation, optimise operations, and enhance how millions of customers engage with our products across ANZ. You’ll work across the full ML lifecycle - from experimentation to production - turning complex data into high-impact, scalable solutions. Your work won’t sit in notebooks; it will directly influence customer experience and business performance. If you’re driven by solving meaningful problems and seeing your models make a measurable difference, this is your game. **A Day in the Life** Expect variety, ownership, and real impact. * Monitor and improve live models, analysing performance, drift, and outcomes * Partner with product and engineering teams to identify high-value ML opportunities * Build and deploy models, feature pipelines, and real-time inference services * Strengthen MLOps practices, improving deployment speed, reliability, and observability * Collaborate with data scientists and engineers to take ideas from concept to production * Contribute to architecture decisions and continuously raise the bar on ML engineering standards **Your Strengths** * Proven experience deploying machine learning models into production * Strong Python skills and hands-on experience with frameworks like PyTorch, TensorFlow, or Scikit-learn * Solid understanding of data pipelines and scala
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