DevOps / MLOps Engineer

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., * Scale and optimise an internal MLOps platform used across multiple MLfocused teams

• Drive automation, testing reliability, and performance improvements across ML pipelines

• Manage and fine-tune GPU-accelerated Kubernetes clusters to support highavailability, 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

• 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 cloudnative 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