ML Ops Engineer

MLOps Engineer

£600 to £650 per day
London
Inside IR35
Hybrid, 2 days per week in the office

This is an opportunity for an MLOps Engineer to work on high-scale, real-time machine learning systems, with a strong focus on deploying and operationalising models in live online environments. The role is approximately 70% MLOps and deployment focused, with additional involvement across machine learning, recommendation systems and LLMs.

The Company
They are a major digital organisation operating high-traffic online platforms, with machine learning playing an important role in the customer experience. Their teams are developing and deploying ML solutions across recommendations, ranking, pricing and product retrieval. This assignment sits within a specialist recommendations team working on live, production ML systems.

The Role and Deliverables

• Deploy and productionise machine learning models within live, online environments.
• Build and support real-time ML systems across recommendations, ranking and pricing.
• Develop MLOps processes to support reliable model deployment and operation.
• Work with ML technologies including TensorFlow, PyTorch and MLflow, with flexibility around the wider technology stack.
• Support LLMOps and the deployment of open-weight LLMs for product retrieval and data processing.
• Work alongside ML specialists to take models from development into scalable production environments.

Your Skills & Experience

• Strong MLOps experience with a clear focus on deploying ML models into production.
• Strong knowledge of real-time, online or high-throughput machine learning systems.
• Experience working with recommendation, ranking, retrieval or similar production ML systems.
• Hands-on capability with TensorFlow, PyTorch, MLflow or comparable ML tooling.
• Strong understanding of ML deployment, productionisation and operational ML infrastructure.
• Experience with LLMOps or deploying LLMs into production environments would be highly beneficial.

How to Apply
If you are an MLOps Engineer with strong production deployment experience and have worked with real-time machine learning systems, apply to find out more.