MLOps Engineer

Understanding Recruitment; Greater London; Híbrido; GBP 90.000 - 130.000; Jornada completa

Descripción de la vacante

Understanding Recruitment Ltd is partnering with a London-based company to hire a Senior MLOps Engineer who will take technical ownership of an AWS-native ML platform.

The role focuses on scaling real ML systems, automating infrastructure, observability, and preparing for GenAI applications, rather than model experimentation. You will drive reliability and performance in production within a lean, ownership-driven team.

Formación

• Experience deploying real ML systems in production.
• Hands-on AWS SageMaker and Python.
• Experience designing ML pipelines and deployment automation.
• Ability to debug complex production issues and make architectural decisions.
• Familiarity with Kafka, Iceberg, Athena or Parquet is a plus.

Responsabilidades

• Own the scaling of the existing AWS-native ML platform.
• Ensure models reach production reliably and scale.
• Automate, observe, and improve infrastructure for ML workloads.
• Make architectural decisions independently.

Conocimientos

Production ML systems

AWS SageMaker

Python

ML pipeline design

Deployment automation

System monitoring

Cloud-native infrastructure

Data engineering

Debug production issues

Architectural decisions

Kafka

Iceberg

Athena

Parquet

Descripción del empleo

MLOps Engineer

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 aSenior MLOps Engineerto 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 forhow 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 operatingreal ML systems in production


• Deep hands-on experience withAWS 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 withKafka, Iceberg, Athena or Parquetwould be a bonus.

You'll be 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.