MLOps Engineer

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London / £500 - £650 day

INFO

Salary

RATE:

£500 - £650

Location

LOCATION

London

Job Type
JOB TYPE

Contract

Contract MLOps / Machine Learning Engineer

£650/day Inside IR35
6-Month Contract
Hybrid London (2 days per week preferred)

We're looking for an experienced MLOps / Machine Learning Engineer to join a high-performing recommendation team within a major consumer technology platform.

This is an engineering-first role focused on deploying and scaling machine learning models in real-time production environments. You'll work on customer-facing recommendation systems, helping deliver personalised experiences at scale while contributing to modern ML and LLM-powered workflows.

What You'll Be Doing

  • Deploying machine learning models into production environments
  • Building and maintaining real-time ML systems
  • Supporting recommendation and ranking engines
  • Working on model serving, monitoring and performance optimisation
  • Deploying neural network and transformer-based models
  • Contributing to product retrieval systems and LLM-powered workflows
  • Collaborating closely with software engineers and data scientists

What We're Looking For

  • Strong experience as an MLOps Engineer or Machine Learning Engineer
  • Proven track record deploying ML models into production
  • Experience with real-time / online ML systems
  • Strong software engineering and Python skills
  • Experience operating and scaling ML infrastructure
  • Excellent communication and stakeholder engagement skills

Nice to Have

  • Recommendation systems
  • Ranking systems
  • Personalisation platforms
  • LLMOps experience
  • TensorFlow
  • PyTorch
  • MLflow
  • Triton

Ideal Backgrounds

We're particularly interested in candidates from:

  • Recommendation & Personalisation
  • E-commerce & Retail Tech
  • Search & Discovery
  • Conversational AI
  • Fraud Detection
  • Gaming
  • High-scale consumer platforms

Contract Details

Initial 6-month contract
£650/day Inside IR35
Hybrid London (2 days per week preferred)
Two-stage interview process

If you have experience deploying machine learning models at scale and enjoy solving real-world production challenges, we'd love to hear from you.


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