Principal Machine Learning Engineer (MLOps)

TL;DR

Principal Machine Learning Engineer (MLOps/GenAI): Designing and scaling MLOps and AIOps frameworks to accelerate the deployment of production-grade ML and AI solutions across core brands with an accent on platform scalability and automation. Focus on building reusable ML infrastructure, optimizing model lifecycles, and implementing secure GenAI/LLM architectures.

Location: Hybrid in London or Gibraltar. Relocation support available depending on existing location.

Company

A global leader in sports betting, gaming, and interactive entertainment focused on innovation and technical excellence.

What you will do

• Lead the design and implementation of scalable MLOps and AIOps frameworks to simplify model training, deployment, and monitoring.
• Build and maintain reusable ML infrastructure components using AWS, Snowflake, Prefect, and CI/CD pipelines.
• Drive the adoption of modern software engineering practices, including IaC, containerization, and automated testing.
• Contribute to the design of GenAI and LLM-based solutions, including agentic workflows and enterprise AI assistants.
• Collaborate with Data Scientists, Cloud Engineers, and Product Owners to translate business requirements into technical solutions.
• Mentor other engineers and define architectural standards and technical roadmaps for ML infrastructure.

Requirements

• Proven experience designing and operating production-grade ML infrastructure and MLOps platforms.
• Strong hands-on expertise with AWS (ML, orchestration, compute, security) and Snowflake.
• Strong Python engineering skills and experience building maintainable, production-ready code.
• Experience with workflow orchestration tools such as Prefect, Airflow, or Dagster.
• Proficiency with Docker and deployment on ECS, EKS, or Kubernetes.
• Deep understanding of the ML lifecycle, including batch/real-time inference and model monitoring.

Nice to have

• Experience with AWS SageMaker, Bedrock, Lambda, or Step Functions.
• Knowledge of MLflow, feature stores, and drift detection patterns.
• Experience with AIOps use cases like anomaly detection and operational intelligence.
• Familiarity with secure GenAI patterns, including guardrails and PII protection.
• Experience in regulated, high-scale digital environments or the igaming industry.

Culture & Benefits

• Competitive salary and private healthcare.
• Hybrid working model.
• Flexible annual leave options (buy/sell/carry over).
• Additional days leave for Christmas Eve or New Year's Eve.
• Relocation support depending on existing location.
• Opportunities for professional growth and development within the business.