ML Ops Engineer

Job summary

The ML Ops Engineer role focuses on building and operating platform capabilities to transition machine-learning models from experimentation to reliable production services. Key responsibilities include automating the ML lifecycle, designing CI/CD pipelines, deploying inference services in cloud environments, and ensuring system reliability and observability through metrics and monitoring.

Qualifications

• Strong production Python skills, including clean APIs, testing, performance awareness and maintainable services
• Practical understanding of the ML lifecycle, including training, validation, inference, model release, monitoring and retraining
• Hands-on experience with at least one workflow or orchestration system used for ML training, validation or deployment
• Ability to reason about system design, reliability and operational trade-offs—not just individual tools
• Clear communication skills and the ability to work effectively with research, engineering, platform, security and product teams

Responsibilities

• Build repeatable workflows for model training, validation, promotion, deployment and retraining
• Productionise models through packaging, versioning, model registry integration, deployment automation and safe rollback
• Design CI/CD pipelines for ML systems, including automated testing, validation, release controls and environment promotion
• Deploy and operate batch and online inference services in containerised cloud environments
• Monitor service health, infrastructure, data-quality signals, data drift, prediction drift and model performance decay
• Improve system robustness, scalability and cost efficiency through automation, observability and infrastructure as code

Skills

Relocation