Join a rapidly scaling AI startup building a proprietary multimodal foundation model for retail. As a Senior MLOps Engineer, you will own the critical infrastructure and tooling that transforms experimental models into reliable, reproducible, and observable production systems, enabling rapid innovation and deployment at scale.
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Senior MLOps Engineer at VC-backed AI-native retail platform
Are you a Senior MLOps Engineer ready to build the backbone of an innovative AI platform? Join a well-funded, rapidly scaling startup that's revolutionizing retail with a proprietary multimodal foundation model. You'll own the infrastructure that brings cutting-edge research to life, tackling challenges in real-time inference and large-scale ML operations. This is your chance to make a significant impact in a high-growth environment.
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Location
London, United Kingdom
Compensation
Not Disclosed
Company
Confidential company
Role overview
About the company
VC-backed AI-native retail platform
What you will do
- Build and maintain robust CI/CD pipelines for model training, evaluation, and deployment across environments.
- Design and implement model registries, versioning systems, and experiment tracking for full reproducibility.
- Instrument comprehensive monitoring for model performance, data drift, prediction quality, and system health.
Who this is a fit for
- Experience building and operating ML infrastructure, ideally in production environments serving real users.
- Strong proficiency in containerisation (Docker, Kubernetes) and orchestration of multi-stage ML workflows.
- Practical knowledge of infrastructure as code, CI/CD best practices, and cloud platforms (AWS, GCP, or Azure).
Why this role is remarkable
- You'll be part of a high-output team, owning infrastructure vital for bringing cutting-edge AI research to customers.
- The company is well-funded, backed by top-tier VCs, and rapidly scaling after a recent Series A.
- You'll tackle complex challenges at the forefront of ML systems, including multi-modal models, real-time inference, and retail-scale operations.
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