Senior MLOps Engineer Outside IR35 | Fully Remote A leading global technology business is seeking a Senior MLOps Engineer to support the evolution and scalability of its machine learning infrastructure. This role offers the opportunity to work on a high-traffic platform with millions of daily data points, enabling meaningful real-world impact through advanced ML systems across areas like content recommendation, safety, and user engagement. The ideal candidate will bring deep experience in managing scalable Kubernetes environments, cloud-native infrastructure, and MLOps tooling, enabling rapid iteration and high-throughput model deployment. Key Responsibilities • Scale and optimise an internal MLOps platform used across multiple ML-focused teams • Drive automation, testing reliability, and performance improvements across ML pipelines • Manage and fine-tune GPU-accelerated Kubernetes clusters to support high-availability, cloud-native workloads • Support production readiness and system reliability through on-call participation and proactive monitoring • Evaluate and implement modern MLOps tooling in alignment with the company’s cloud and ML strategy • Collaborate closely with machine learning engineers and product stakeholders to ensure infrastructure meets evolving project demands • Share knowledge across teams to elevate engineering standards in DevOps, MLOps, and infrastructure reliability Desired Experience • Strong experience managing GPU-enabled Kubernetes clusters at scale • Deep understanding of the full ML lifecycle: experimentation, training, deployment, versioning, and monitoring • Proficiency in languages like Python, Go, or similar, with an emphasis on automation and ML tooling • Proven track record building infrastructure that accelerates experimentation and model deployment in cloud environments • Familiarity with CI/CD tools such as ArgoCD, GitHub Actions, or similar, especially for ML use cases • Experience with observability tools such as Prometheus, Grafana, and cloud-native monitoring solutions • Comfortable contributing to incident response and participating in an on-call rotation • Solid experience with containerisation technologies like Docker in hybrid or fully cloud-native environments • Working knowledge of Terraform and Infrastructure-as-Code principles • Keen interest in emerging MLOps technologies and cloud-native best practices • Self-motivated, inquisitive, and passionate about continuous learning • Experience with AWS or similar cloud platforms is highly desirable, especially in the ML domain