Senior Machine Learning Engineer (MLOps)

Location: London, England, United Kingdom; Workplace type: Hybrid

Company

hirify.global is an online fashion retailer operating a customer-facing platform used by millions worldwide.

What you will do

• Design and build scalable machine learning platforms and infrastructure for model training, deployment, and serving.
• Develop highly available backend services supporting recommendation, search, personalisation, and AI experiences.
• Build CI/CD pipelines, automated deployment processes, tooling, and frameworks for ML and data products.
• Design batch and real-time inference architectures using cloud-native technologies.
• Improve reliability, resilience, performance, observability, monitoring, and resource utilisation across ML workloads.
• Own production services, incident management, root cause analysis, architectural decisions, platform strategy, and engineer mentoring.

Requirements

• Strong software engineering fundamentals and experience building production systems at scale.
• Experience with distributed systems, microservices, high-throughput backend platforms, data-intensive systems, streaming, or large-scale distributed processing.
• Strong programming skills in Python, Java, Kotlin, Go, Scala, or similar languages.
• Experience operating services in AWS, Azure, or GCP, with Kubernetes, containerisation, and cloud-native technologies.
• Experience implementing CI/CD, automated deployments, Infrastructure-as-Code, and monitoring, alerting, or observability tooling; relevant tools include Terraform, Pulumi, and CloudFormation.
• Experience building reliable, resilient, scalable systems with a focus on performance and operational excellence.
• Experience with machine learning infrastructure, model serving, feature stores, training infrastructure, MLOps, and large-scale model training or inference workloads.
• Technical leadership, mentoring, architectural influence, and strong collaboration and communication in cross-functional product teams.
• Knowledge of vector search, ranking, retrieval architectures, recommendation platforms, internal developer platforms, engineering enablement tooling, LLMs, Generative AI, or production AI systems.

Nice to have

• Experience supporting recommendation systems, search platforms, personalisation engines, or other customer-facing data products.

Culture & Benefits

• Full-time role with a hybrid work arrangement in London.
• Employee discount and access to employee sample sales.
• 25 days of paid annual leave plus an additional celebration day.
• Discretionary bonus scheme and flexible benefits allowance, available as extra cash or other benefits.
• Private medical care and personalised learning opportunities.