Principal Machine Learning Engineer
Job Description
Entain is hiring a Principal Machine Learning Engineer to join our wider Enterprise DS&AI team that supports our core Brands and Regions.
As a Principal Machine Learning Engineer, you will support, design, develop, deploy, and maintain advanced software and infrastructure capabilities within the specialised technical domain of Machine Learning Engineering, MLOps, AIOps, and GenAI enablement.
Reporting to the ML Engineering Manager, you will be part of the Enterprise DS&AI Centre of Excellence, focused on creating, scaling, and enhancing machine learning platforms, frameworks, and operational capabilities of significant complexity.
You will play a key role in helping Data Science and AI teams move faster, operate more reliably, and deliver production-grade ML and AI solutions across the business.
Are you ready to be part of our journey delivering technical excellence and collaborating with one of the world’s biggest online gaming and entertainment groups?
What you will do
As a Principal ML Engineer, you will:
Lead the design and implementation of scalable MLOps and AIOps frameworks that simplify how Data Science teams develop, train, deploy, monitor, and maintain machine learning models.
Design, build, and maintain reusable ML infrastructure components using AWS services, Snowflake, Prefect, CI/CD pipelines, and other enterprise-grade tools.
Provide technical leadership across ML engineering initiatives, ensuring solutions are robust, secure, scalable, observable, and aligned with engineering best practices.
Support the development of ML platform capabilities covering experimentation, model training, orchestration, deployment, monitoring, retraining, incident management, and governance.
Collaborate closely with Data Scientists, ML Engineers, Data Engineers, Cloud Engineers, Product Owners, and business stakeholders to understand requirements and translate them into practical technical solutions.
Contribute to the design and implementation of GenAI and LLM-based solutions, including enterprise AI assistants, agentic workflows, AI automation, and secure access to foundation models.
Build frameworks, templates, standards, and reference implementations that reduce duplicated effort and accelerate delivery across multiple Data Science teams.
Drive the adoption of modern software engineering practices, including automated testing, infrastructure as code, containerisation, CI/CD, IaC, model versioning, and production monitoring.
Support orchestration and automation of ML workloads using tools such as Prefect, AWS-native services, and event-driven patterns.
Help define architectural standards and technical roadmaps for ML infrastructure, MLOps, AIOps, and GenAI capabilities.
Review technical designs and code, mentor other engineers, and promote high engineering standards across the team.
Identify operational risks, technical debt, and platform limitations, and propose pragmatic improvements.
Entain is hiring a Principal Machine Learning Engineer to join our wider Enterprise DS&AI team that supports our core Brands and Regions.
As a Principal Machine Learning Engineer, you will support, design, develop, deploy, and maintain advanced software and infrastructure capabilities within the specialised technical domain of Machine Learning Engineering, MLOps, AIOps, and GenAI enablement.
Reporting to the ML Engineering Manager, you will be part of the Enterprise DS&AI Centre of Excellence, focused on creating, scaling, and enhancing machine learning platforms, frameworks, and operational capabilities of significant complexity.
You will play a key role in helping Data Science and AI teams move faster, operate more reliably, and deliver production-grade ML and AI solutions across the business.
Are you ready to be part of our journey delivering technical excellence and collaborating with one of the world’s biggest online gaming and entertainment groups?
What you will do
As a Principal ML Engineer, you will:
Lead the design and implementation of scalable MLOps and AIOps frameworks that simplify how Data Science teams develop, train, deploy, monitor, and maintain machine learning models.
Design, build, and maintain reusable ML infrastructure components using AWS services, Snowflake, Prefect, CI/CD pipelines, and other enterprise-grade tools.
Provide technical leadership across ML engineering initiatives, ensuring solutions are robust, secure, scalable, observable, and aligned with engineering best practices.
Support the development of ML platform capabilities covering experimentation, model training, orchestration, deployment, monitoring, retraining, incident management, and governance.
Collaborate closely with Data Scientists, ML Engineers, Data Engineers, Cloud Engineers, Product Owners, and business stakeholders to understand requirements and translate them into practical technical solutions.
Contribute to the design and implementation of GenAI and LLM-based solutions, including enterprise AI assistants, agentic workflows, AI automation, and secure access to foundation models.
Build frameworks, templates, standards, and reference implementations that reduce duplicated effort and accelerate delivery across multiple Data Science teams.
Drive the adoption of modern software engineering practices, including automated testing, infrastructure as code, containerisation, CI/CD, IaC, model versioning, and production monitoring.
Support orchestration and automation of ML workloads using tools such as Prefect, AWS-native services, and event-driven patterns.
Help define architectural standards and technical roadmaps for ML infrastructure, MLOps, AIOps, and GenAI capabilities.
Review technical designs and code, mentor other engineers, and promote high engineering standards across the team.
Identify operational risks, technical debt, and platform limitations, and propose pragmatic improvements.
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