Company Description
Sports betting, gaming and interactive entertainment is changing, and we’re leading that change. By putting people first. By placing exciting, engaging and entertaining experiences at more fingertips than ever before. We're pulling into pole position by pushing boundaries further. With innovation. With technology. But most importantly, with people like you. Because when you join Entain, it’s your game. So let’s win together.
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:
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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.
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Design, build, and maintain reusable ML infrastructure components using AWS services, Snowflake, Prefect, CI/CD pipelines, and other enterprise-grade tools.
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Provide technical leadership across ML engineering initiatives, ensuring solutions are robust, secure, scalable, observable, and aligned with engineering best practices.
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Support the development of ML platform capabilities covering experimentation, model training, orchestration, deployment, monitoring, retraining, incident management, and governance.
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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.
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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.
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Build frameworks, templates, standards, and reference implementations that reduce duplicated effort and accelerate delivery across multiple Data Science teams.
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Drive the adoption of modern software engineering practices, including automated testing, infrastructure as code, containerisation, CI/CD, IaC, model versioning, and production monitoring.
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Support orchestration and automation of ML workloads using tools such as Prefect, AWS-native services, and event-driven patterns.
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Help define architectural standards and technical roadmaps for ML infrastructure, MLOps, AIOps, and GenAI capabilities.
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Review technical designs and code, mentor other engineers, and promote high engineering standards across the team.
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Identify operational risks, technical debt, and platform limitations, and propose pragmatic improvements.
Qualifications
What we are looking for
We are looking for an experienced technical leader with strong hands-on engineering capability and a deep understanding of how to bring machine learning systems into production.
You should have:
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Strong experience as a Machine Learning Engineer, MLOps Engineer, AI Platform Engineer, Cloud ML Engineer, or similar role.
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Proven experience designing and operating production-grade ML infrastructure and MLOps platforms.
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Strong hands-on experience with Cloud providers (AWS), especially services related to machine learning, orchestration, compute, storage, networking, security, and deployment.
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Experience with Snowflake as a data platform, including data access patterns, integration with ML workflows, and performance-aware data consumption.
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Experience with workflow orchestration tools such as Prefect, Airflow, Dagster, or similar.
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Strong experience implementing IaC and CI/CD pipelines for software, data, and machine learning workflows.
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Strong Python engineering skills and experience building maintainable, tested, production-ready code.
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Experience with containerisation using Docker, and ideally deployment on ECS, EKS, Kubernetes, or equivalent platforms.
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Good understanding of model training, batch inference, real-time inference, model monitoring, retraining, and ML lifecycle management.
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Experience working with stakeholders to gather requirements, shape technical solutions, and coordinate delivery across multiple teams.
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Exposure to GenAI, LLMs, AI agents, RAG architectures, prompt orchestration, or enterprise AI assistant implementations.
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Ability to define technical standards, influence architecture, mentor engineers, and guide teams through complex technical decisions.
Nice to have
Experience with any of the following would be beneficial:
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AWS SageMaker, Bedrock, Lambda, ECS, EKS, Step Functions, EventBridge, CloudWatch, IAM, S3, ECR, or related services.
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MLflow, model registries, feature stores, model observability, drift detection, and automated retraining patterns.
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AIOps use cases such as anomaly detection, incident enrichment, automated ticket creation, event correlation, alert deduplication, or operational intelligence.
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Building internal developer platforms, reusable frameworks, project templates, or self-service capabilities for technical teams.
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Secure GenAI patterns, including guardrails, access control, PII protection, auditability, and model governance.
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Experience in regulated, high-scale, or customer-facing digital environments.
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Knowledge of online gaming, entertainment, betting, or high-volume transactional platforms.
Key behaviors and capabilities
You will be successful in this role if you are:
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Technically strong and comfortable operating at both architectural and implementation levels.
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Pragmatic, delivery-focused, and able to balance ideal engineering practices with business priorities.
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Able to simplify complex technical concepts for different audiences.
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Comfortable influencing without always having direct authority.
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Proactive in identifying risks, gaps, and opportunities for improvement.
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Passionate about creating reusable frameworks and reducing operational friction for other teams.
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A strong collaborator who can work across Data Science, Engineering, Cloud, Security, Product, and business functions.
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Committed to engineering excellence, automation, reliability, and continuous improvement.
Additional Information
At Entain, we know that signing top players requires a great starting package, and plenty of support to inspire peak performance. Join us, and a competitive salary is just the beginning. Working for us in our London or Gibraltar offices, you can expect to receive great benefits like:
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Competitive salaries
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Option to buy/ sell and carry over annual leave
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Additional days leave to take on Christmas eve or NYE
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Option to buy and sell and move over annual leave
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Relocation support depending on existing location
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Private Healthcare
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Hybrid working
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Room to grow and develop throughout the business!
And outside of this, you’ll have the chance to turn recognition from leaders and colleagues into amazing prizes. Join a winning team of talented people and be a part of an inclusive and supporting community where everyone is celebrated for being themselves.
Should you need any adjustments or accommodations to the recruitment process, at either application or interview, please contact us.
