MLOps Engineer

Understanding Recruitment; Greater London; Hybrid; GBP 90,000 - 120,000; Full time

Job summary

Understanding Recruitment in London is seeking a Senior MLOps Engineer to own an AWS-native ML platform in a hybrid setting. The role focuses on scaling production ML systems, not experimental work.

You will shape deployment pipelines, monitoring, automation, and observability, and make architectural decisions independently for a live data lake and GenAI foundations.

Qualifications

• Production ML systems experience in real environments.
• Deep hands-on AWS SageMaker and Python expertise.
• Experience designing ML pipelines and deployment automation.
• Solid understanding of cloud-native infrastructure and data engineering.
• Ability to debug complex production issues and make architectural decisions independently.
• Experience with Kafka, Iceberg, Athena or Parquet is a bonus.

Responsibilities

• Own how ML models reach production and scale in a live AWS environment.
• Make systems more automated, observable and scalable.
• Lead architectural decisions and drive platform ownership within a lean team.

Skills

Production ML systems

AWS SageMaker

Python

ML pipelines

Deployment automation

Monitoring

Cloud-native infrastructure

Data engineering

Debug production issues

Kafka

Iceberg

Athena

Parquet

Tools

Kafka

Iceberg

Athena

Parquet

Job description

London (Hybrid) | Python | ML Infrastructure | High Ownership

Models are already in production. The infrastructure is live. Now they need someone to take ownership of how it all scales.

I'm working with a company looking for a Senior MLOps Engineer to take technical ownership of its AWS-native machine learning platform.

This isn't a role focused on experimenting with models or simply maintaining cloud infrastructure.

You'll be responsible for how ML models actually reach production, perform reliably and continue to improve at scale.

The platform is already operational, with an established AWS environment and a ~5TB data lake. The next challenge is making the infrastructure more automated, observable and scalable, while laying the foundations for future GenAI applications.

What they're looking for:

• Strong track record of deploying and operating real ML systems in production
• Deep hands-on experience with AWS SageMaker and Python
• Experience designing ML pipelines, deployment automation and monitoring
• Solid understanding of cloud-native infrastructure and data engineering
• Someone comfortable debugging complex production issues and making architectural decisions independently
• Experience with Kafka, Iceberg, Athena or Parquet would be a bonus.

You’re the go-to engineer for MLOps within a lean technical team, with genuine ownership over the platform rather than responsibility for one small part of a larger system.