Senior AI Operations Engineer (MLOps)

XpertDirect • United Kingdom
Remote
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AI Summary

Deploy, monitor, and optimize machine learning systems at scale. Collaborate with cross-functional teams to deliver robust AI products. 6-month contract, remote work.

Key Highlights
Deploy and manage ML models in production
Build and maintain MLOps pipelines
Monitor model performance and operational health
Key Responsibilities
Deploy and manage machine learning models in production environments
Build and maintain MLOps pipelines and automation workflows
Monitor model performance, reliability, and operational health
Support model versioning, testing, and deployment processes
Improve scalability, observability, and operational efficiency across AI platforms
Collaborate with engineering and data science teams to optimize AI delivery workflows
Technical Skills Required
Python Kubernetes MLflow AWS or GCP CI/CD Pipelines
Benefits & Perks
Fully remote contract engagement
Work on cutting-edge AI products
Modern cloud-native technology stack
Nice to Have
Experience with LLMs and Generative AI platforms
Infrastructure as Code (Terraform or similar)
Model monitoring and observability tooling
Feature stores and vector databases
Experience supporting large-scale AI platforms

Job Description


AI Operations Engineer (MLOps)

Remote (Company based in London, UK)

Contract | 6-Month Initial Contract

AI | Machine Learning Platforms


Machine learning models are only as valuable as their ability to operate reliably in production.

For one of our clients, a fast-growing AI technology scale-up based in London, we are looking for an AI Operations Engineer (MLOps) to help deploy, monitor, and optimise machine learning systems operating at scale.


You'll work closely with machine learning engineers, data scientists, and platform teams to build the infrastructure, automation, and operational processes required to move AI solutions from experimentation to production.


This is an excellent opportunity for an experienced contractor who enjoys solving complex infrastructure challenges while helping to deliver robust, scalable AI products.


Technical Environment

• Python

• Kubernetes

• MLflow

• AWS or GCP

• CI/CD Pipelines


What You'll Be Doing

• Deploying and managing machine learning models in production environments

• Building and maintaining MLOps pipelines and automation workflows

• Monitoring model performance, reliability, and operational health

• Supporting model versioning, testing, and deployment processes

• Improving scalability, observability, and operational efficiency across AI platforms

• Collaborating with engineering and data science teams to optimise AI delivery workflows


Experience Required

• 4+ years in MLOps, Platform Engineering, DevOps, or Machine Learning Engineering

• Experience deploying machine learning systems into production

• Strong understanding of cloud-native infrastructure and automation practices

• Experience working with CI/CD pipelines and containerised environments

• Familiarity with machine learning lifecycle management and monitoring


Nice to Have

• Experience with LLMs and Generative AI platforms

• Infrastructure as Code (Terraform or similar)

• Model monitoring and observability tooling

• Feature stores and vector databases

• Experience supporting large-scale AI platforms


Why This Role?

✔ Fully remote contract engagement

✔ Work on cutting-edge AI products

✔ Modern cloud-native technology stack

✔ High-growth scale-up environment

✔ Opportunity to help shape the operational foundations of AI at scale


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