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.
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MLOps Engineer employer: Understanding Recruitment
As a leading GreenTech company based in London, we pride ourselves on fostering a dynamic and inclusive work culture that prioritises innovation and sustainability. Our employees enjoy a competitive benefits package, flexible working arrangements, and ample opportunities for professional growth, all while contributing to impactful clean energy projects that make a difference in the world.
