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.
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